Singularities of configuration and graph hypersurfaces
Transcription
Singularities of configuration and graph hypersurfaces
THE UNIVERSITY OF CHICAGO SINGULARITIES OF CONFIGURATION AND GRAPH HYPERSURFACES A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE PHYSICAL SCIENCES IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF MATHEMATICS BY ERIC PATTERSON CHICAGO, ILLINOIS JUNE 2009 UMI Number: 3362457 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. UMI UMI Microform 3362457 Copyright 2009 by ProQuest LLC All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 To my gardener ABSTRACT I show that the singularities of a configuration hypersurface correspond to the points whose corank is at least two. The result generalizes to compute multiplicities at higher corank loci in configuration hypersurfaces. Though the result is similar to the case of a generic determinantal variety, the proof proceeds from a different perspective. The key is simple linear algebra: the rank of a quadratic form is determined by its ranks when restricted to each hyperplane in a complete set of hyperplanes. I review the construction of the first and second graph hypersurfaces as examples of configuration hypersurfaces. In particular, the singularities of the first and second graph hypersurfaces are corank at least two loci. As an application, the singularities of the first graph hypersurface are contained in the second graph hypersurface, and the singularities of the second graph hypersurface are contained in the first hypersurface. I show that the map to which the graph hypersurface is dual is not an embedding in general. Therefore, the incidence variety may not resolve the singularities of the graph hypersurface. I present a formula that describes the graph polynomial of a graph produced by a specific gluing-deleting operation on a pair of graphs. The operation produces logdivergent graphs from log-divergent graphs, so it is useful for generating examples relevant for physics. A motivic understanding of the operation is still missing. m ACKNOWLEDGMENTS I am in great debt to my advisor, Spencer Bloch. He introduced me to the subject of graph hypersurfaces and was a source of infinitely many ideas, questions, and suggestions during the research process. I greatly appreciate the direction and freedom he gave me, and his encouragement was indispensable in the completion of this work. Moreover, he has worked quickly and tirelessly to help me meet every deadline even though I never left sufficient time. His role cannot be overstated. I would like to thank Bob Kottwitz for sitting on my committee and for reading the manuscript under extreme time constraints. His willing assistance during my graduate education has been a boon of utmost importance. I learned Theorem 4.4.8 from Laci Babai while working for his discrete math course for the REU in 2004. At that time, I had no idea it would figure prominently in this work. I am grateful to him for this contribution. I would like to thank the Department of Mathematics at the University of Chicago for its generous support and wonderful academic environment, which I often took for granted but were instrumental in the completion of this work. I must thank Gabe Kerr for his interest in this project, especially during its infancy. While we were never successful in implementing his approaches, I learned an enormous amount from his suggestions. In the final months of this project, Adam Ginensky has provided many valuable contributions. His interest and enthusiasm alone has been a great motivator. He also pointed me to many useful resources and perspectives. These suggestions have greatly improved this project. I regret that I have not been able to make a precise connection between his work and mine in the present work. That will have to be the topic of future work. His input on the writing process has prevented much anguish and provided many improvements to the manuscript. I am grateful for all of these contributions. iv V Through all my years at the University of Chicago, Nick Longo has been a valued colleague and friend. His door was always open for all matters mathematical and otherwise. Nick provided a key element: coffee. Before Nick's influence, I failed to recognize the efficacy of this wonderful drug, without which I doubt I would have completed the present work. He also provided all of the necessary threats to motivate me, and by having twice as much to do as me at all times, he provided a great example to emulate. There are two common features to almost all Ph.D.'s. First, there is the decision to start the Ph.D. Second, as Spencer Bloch once put it, "The point of graduate school is to graduate." Regarding the first, I must thank everyone who inspired me to begin this endeavor, especially Abhijit Champanerkar, Bob Friedman, Patrick Gallagher, Ilya Kofman, Max Lipyanskiy, Henry Pinkham, Michael Prim, and Greg Smith. Regarding the second, I must thank WH Trading whose light at the end of the tunnel motivated me to get out of the tunnel. Finally, I must thank and apologize to everyone who put up with my abuse and neglect for the past six years, particularly my students and my family. My parents have been more supportive and encouraging throughout graduate school and my life than I could ever express. Lauren Duensing, has been by my side through everything. Her patience is extraordinary. She is completely responsible for preserving my sanity, for which I am eternally grateful. She has also done a tremendous job of preventing extensive abuse of the English language by thoroughly proofreading and generally providing grammatical advice. And Shelby has been my constant companion during all of my writing and research—thank you, Shel. INTRODUCTION This dissertation essentially proves a few facts about some very specific degeneracy loci. In simple terms, the motivating questions of this dissertation are: 1. Is the dual of a graph hypersurface smooth? 2. How are the first and second graph hypersurfaces related, and where do they intersect? I find partial answers to each question, but an important contribution of this dissertation is a result discovered while trying to answer the second question. Namely, the singular locus of the graph hypersurface, and more generally any configuration hypersurface, is the same as the corank at least 2 locus when thought of as quadratic forms. The motivation for both questions is that the defining polynomials of the first and second graph hypersurfaces appear in parametric Feynman integrals [IZ80]. Broadhurst and Kreimer [BK95,BK97] numerically calculated values of these integrals and discovered that they are (at least approximately) multizeta values. Their discovery interested algebraic geometers who view the integrals as periods of the first graph hypersurface. That these periods may be multizeta values suggests trying to apply the techniques of motives, which already establish connections between geometry and multizeta values [Bor77, Sou86, DG05]. Belkale and Brosnan [BB03] show that the graph hypersurfaces are not mixed Tate in general. Therefore, whether the periods are multizeta values requires more delicate machinery to identify whether these particular periods are multizeta values and/or which graphs have multizeta periods. Bloch, Esnault, and Kreimer [BEK06] verified that the motives of the wheeland-spokes graph hypersurfaces are consistent with the period being a zeta value. Brown [Bro09] has developed algorithmic methods for evaluating these integrals and determines a criterion to determine whether particular graphs evaluate to multizeta vi vii values. The criterion is strong enough to find new results but is not sufficient for all graphs. Aluffi and Marcolli have studied graph hypersurfaces from the point of view of the Grothendieck group and Chern-Schwartz-MacPherson classes [AM08] and have addressed the mixed Tate issue under combinatorial conditions on the graph [AM09]. The work of Brown, Aluffi, and Marcoli draws attention to working with the affine hypersurface that is the cone over the graph hypersurface; my results translate to the affine case, as well. Their work also suggests how the complexity of the topology of the graph (as measured by its crossing number and its genus) may be influencing the complexity of the multizeta values (as in multizeta values are more complex than zeta values). The first graph polynomial has received more attention than the second in the algebraic geometry literature because a convergent parametric Feynman integral is a period for the first graph hypersurface, not the second. However, a more complete understanding of these periods will require a better understanding of the second hypersurface, as well. A configuration hypersurface is a degeneracy locus naturally associated with a nonzero subspace of a based vector space. The paper [BEK06] shows that the first graph hypersurface is a configuration hypersurface; following the same ideas, I show in Proposition 4.2.12 that the same is true for the second graph hypersurface. The motivation for the first question is that if the dual of the graph hypersurface were smooth, then the incidence variety defining the dual would resolve the singularities of the graph hypersurface. The paper [BEK06] noted that the graph hypersurface is naturally a dual variety when a specific quadratic linear series is an embedding, and one could hope that the explicit nature of the incidence variety would provide a useful resolution. In Theorem 4.4.1, I show that the quadratic linear series is not an embedding in general, so the incidence variety may not resolve the singularities. There are exceptional graphs for which the quadratic linear series is an embedding, but they must satisfy strong conditions. Depending on the reader's background, this result may require very little or very much new material. I present this result last as it is not a prerequisite for the other geometric results of the paper. viii Readers interested only in the graph theory may skip to Chapter 4; the prerequisite geometry for this result is in Section 2.2. My partial answer to the second question, Corollary 4.2.15, is that the singular loci of both the first and second graph hypersurfaces are contained in the intersection of the two graph hypersurfaces. I discovered this result by a careful analysis of restrictions of configurations. I show in Section 4.2 that the singularities of the graph hypersurfaces agree with the corank at least two locus. This result, interesting in its own right, also provides a tool for comparing the singular loci of the hypersurfaces of subconfigurations such as the first and second graph hypersurfaces. Moreover, I show that the points of multiplicity k correspond to the corank k locus, and I show this for configuration hypersurfaces, of which graph hypersurfaces are a special case. These results are discussed in Chapter 3 and depend on the preliminaries presented in the preceding chapters. There are two other results I present that I would like to point out. First, Corollary 1.2.24 shows that the rank of a degenerate bilinear form will remain the same on at least one hyperplane in a complete set of hyperplanes. This is the generic behavior for hyperplanes, and the complete condition implies that one of the hyperplanes is generic in this sense. This result is not true for nonsymmetric bilinear forms. This corollary and its generalization, Lemma 1.2.4, are essential in the proof that the singular locus of a configuration hypersurface is the corank greater than one locus. Because it is an interesting linear algebra result in its own right, including its proof seems preferable to giving a reference. Second, I present a graph operation that glues graphs along an edge and deletes that edge. This operation is useful for the study of log-divergent graphs because from two log-divergent graphs it produces another log-divergent graph. Therefore, it is a useful operation for writing down high-loop, log-divergent examples. In Proposition 4.3.19, I derive the effect of this operation on the graph polynomial. The geometry of this formula, and in particular how the graph motive is affected, needs further development. Physicists suggest that the Feynman amplitudes corresponding to this operation will be a product, so one would hope to find a product structure IX reflected in the motive. There have been many approaches to studying graph hypersurfaces in the literature. Many approaches use the combinatorics of the graphs to make conclusions about the hypersurface; Theorem 4.4.1 falls into that category. My approach to Corollary 4.2.14 uses the linear algebra construction of the graph polynomial. This construction can be traced back to Kirchhoff using graph Laplacians to study electrical networks. In fact, alternative names for the graph polynomials in the literature are Kirchhoff polynomials and Kirchhoff-Symanzik polynomials (Symanzik applied them in quantum field theory). The more recent literature [BEK06] emphasized this approach, and my progress would have been impossible without the philosophy of that source. In Chapter 1, I introduce the bilinear form notation and terminology that I will use, including the geometry of quadrics. The key result of this chapter is Lemma 1.2.4, which describes constraints on the rank of a bilinear form in terms of its rank on its subspaces. Chapter 2 discusses some general geometric constructions of families of quadratic forms, namely degeneracy loci in Section 2.1 and quadratic linear series in Section 2.2. The remaining chapters study specific cases of these constructions. The goal of Chapter 3 is to apply Lemma 1.2.4 in the case of certain degeneracy loci defined for configurations. The results of this chapter are about rank loci of specific families of symmetric matrices spanned by rank one matrices. I begin in Section 3.1 by recalling the definition of this family and its degeneracy locus, the configuration hypersurface. Its defining polynomial has a simple description in terms of Pliicker coordinates, and I explain how this varies when restricting the configuration in Section 3.2. In Section 3.3, I exploit these descriptions to find that the singularities of a configuration hypersurface and their order are determined by the rank of these points as matrices. This connection between singularity and rank is applied to subconfigurations in Section 3.4, which is used in Chapter 4 to compare the first and second graph hypersurfaces. X In Chapter 4, I apply the preceding results to the case of graph hypersurfaces. I start with a brief review of the necessary combinatorics and algebraic topology of graphs in Section 4.1. I continue by describing two configurations that come from graphs. Computing their configuration polynomials reveals the familiar combinatorial definitions of the graph polynomials in Section 4.2. I proceed to state the singularity result for the graph hypersurfaces, which does not require anything new by that point. In Section 4.3, I explain some of the ways in which the special nature of these polynomials reflects operations on graphs. These operations and their effects on the polynomials are well-known and well used in the literature, but I have included one formula (4.6), perhaps not as well-known, that is lacking a geometric interpretation. To the extent possible, my arguments emphasize linear algebra coming from the algebraic topology of graphs rather than combinatorics. The final Section 4.4 describes how the quadratic linear series of the graph configuration is not an embedding for most graphs. CONTENTS ABSTRACT iii ACKNOWLEDGMENTS iv INTRODUCTION vi LIST OF FIGURES xii CHAPTER 1. DEGENERATE BILINEAR FORMS 1.1 Basics of Bilinear Forms 1.2 Restriction of Bilinear Forms and Rank Lemmas CHAPTER 2. FAMILIES OF BILINEAR FORMS 1 2 7 32 2.1 Degeneracy Loci 32 2.2 Quadratic Linear Series 38 CHAPTER 3. CONFIGURATIONS 48 3.1 Configuration Hypersurfaces 49 3.2 Restrictions and Projections of Configurations 62 3.3 Singularities of Configuration Hypersurfaces 76 3.4 Subconfigurations 85 CHAPTER 4. GRAPH HYPERSURFACES 90 4.1 Graph Preliminaries 90 4.2 Configurations from Graphs 105 4.3 Polynomial Formulas for Some Graph Operations 123 4.4 Quadratic Linear Series Defined by Graphs 146 REFERENCES 161 XI FIGURES 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 A typical graph A circuit with 6 edges and vertices A circuit with three edges Spanning trees of the circuit with three edges Two-trees of the circuit with three edges Minimal cut sets of the circuit with three edges A simple graph to demonstrate the first graph polynomial A circuit with two edges; a banana Two banana graph Two bananas, one edge split The graphs of Figures 4.9 and 4.10 glued along e, then with e removed to create a bubble on edge e^ of the two banana graph. . . . Two banana graph, revisited A wheel with three spokes A complete bipartite graph, K33 xn 91 100 102 102 103 103 105 141 141 143 144 148 149 150 CHAPTER 1 D E G E N E R A T E BILINEAR FORMS This is a chapter on the linear algebra of bilinear forms. The theory of bilinear forms is well-known, especially the classification of nondegenerate bilinear forms. Chapter 4 of Serre's text [Ser73] provides an elegant, concise treatment. The reader looking for a more complete treatment should consult [Bou07]. An algebro-geometric perspective can be found in [Har92,Del73]. This chapter will gently introduce the results necessary for what follows. The reader familiar with degenerate bilinear forms may prefer to skip this chapter. This chapter introduces the key definitions and properties of bilinear forms that I will use. In the following chapters, I study specific families of bilinear forms and the locus of forms in families that are degenerate. As preparation, I begin in Section 1.1 with the basics of bilinear forms and their radicals. In Section 1.2, I prove Lemma 1.2.4 and Corollary 1.2.24, which relate the rank of a degenerate bilinear form to its rank when restricted to subspaces. In the case of a symmetric bilinear form B on a vector space V, Corollary 1.2.24 says that if B is degenerate on V, then ranky B — r a n k # B for some hyperplane in a complete set of hyperplanes in V. This equality is generically true for hyperplanes: the radical of B will meet a generic H in the radical of B\JJ. However, the equality may not hold for a specific hyperplane. The point of the corollary is that if one has enough hyperplanes (a complete set), then the equality must hold for one of them. These corollaries are the key to relating the singular and rank loci of configuration hypersurfaces in Theorem 3.3.6. This result may be familiar to the reader, in which case Section 1.2 may be skipped. 1 1.1 Basics of Bilinear Forms Let V be a finite dimensional vector space over a field K; denote its dimension by n. Denote the dual space by V v = Hom^-(V, K). A bilinear form B on V is an element of (V ®V)y = Vy ®Vy. Suppose that (5 = {b\,..., bn} is a basis of V. Then the matrix Mp = (B(bi, bj)) represents B in this basis in the sense that B(v,w) = v^Mpwp for all v,w 6 V, where the right side is multiplication of matrices written in the coordinates of the basis (6. Here we consider vp and wp as column vectors in the basis d, and v'o denotes the transpose of the column vector vp. In other words, there are unique v\,..., v ih (^^e coordinates of v in p = {b\,..., vn G K such that v — YA=I bn}), which I express as the column vector I v\ \ v2 V0 \vnJ and similarly for w. By bilinearity of B and the definition of matrix multiplication, both B(v,w) and VgMrjwp are equal to ]T ViB(bi,bj) Wn i,j=l If we represent B in another basis 7 = { c i , . . . , c n } , then the matrix M 7 = (-B(CJ, Cj)) is related to Mp as follows. There exist unique scalars S^ in K such that bj = X^fc=i Sikck- This change-of-basis matrix 5* = (So*) transforms column vectors in the new basis to column vectors in the old basis: vp = Sv1. 3 Therefore, for all v, w 6 V, B(v,w) = VgMpwg {Sv1)tMiiSw1 = = and M 7 = StMpS. vt1StMfjSw1 Therefore, det M 7 = (det S)2 det Mg, so the determinant is not a well-defined invariant of the bilinear form. x 0 x K x /(K )i ; this element is the discriminant It is a well-defined element of of £?. The bilinear form B defines two linear maps from V to V^, rB and £B: 0 -> r a d f l ( F , B)^V rB(v) ^ V = v B(-,v) and 0 - ^ r a d L ( ^ , £ ) -^ V ^ £B(v) = Vw B(v,-). Definition 1.1.1 (Left and Right Radicals). The right radical of B in V, rad/,-(V, B), is rad i ? (V, B) = Ker r B = {v e V | B(w,v) = 0 for all w eV}, and the /e/t radical of B in V, rad£ / (l / , B), is rad L (V, 5 ) = K e r ^ j = {v e V | B(t;,u>) = 0 for all w e V). These subspaces of V may be different if B is not symmetric, but they have the same dimension because row and column rank always agree. I will often work with one bilinear form B and restrict it to various subspaces W of V. In that case, I may suppress the B from the notation and write radjj V for rad^(V, B), r a d # W 4 for rad^W, B\yy), and similarly for the left radicals. When B is symmetric, I will suppress the left and right subscripts from the notation. The corank of B on V, c o r a n k B , is corank i? = d i m r a d ^ V , B) = dimrad^>(V~, B) = dim V — rank B. When I restrict B to a subspace W, I will denote rankB\yy and corankB\yy by rankpy B and coranki^/ B. In particular, r a n k y B and coranky B are synonyms for rank-B and corank B that include V in the notation. The following lemma is wellknown, and I will not include the proof. I make frequent use of the equivalence of these conditions, often without comment. L e m m a 1.1.2 (Characterization of Degenerate/Nondegenerate Bilinear Forms). Suppose that B is a bilinear form on V and W is a nonzero subspace of V. following conditions on B\\y 1. B is nondegenerate 2. r&dL(W,B) =0, 3. rndR(W,B) = 0, are equivalent: on W, 4- corankjy B = 0, 5. rankj^/ B = dim W, and 6. detMg Similarly, ^ 0 for every matrix MQ representing B\\y in a basis /3 ofW. the following are equivalent: 1. B is degenerate, 2. mdL(W,B) 3. T^O, radR(W,B)^0, 4- corankjy B > 0, The 5 5. ranlqy B < dim VK, and 6. det MR = 0 for every matrix Ma representing B\\y in a basis 13 of W. More specifically, the following are equivalent: 1. corankjy B > dim W — k, 2. rank^/ B < k, and 3. all kxk minors of MQ vanish for a matrix Mfj representing B\w in some basis (3 ofW. A bilinear form B is symmetric if B(v, w) = B(w, v) for all v, w e V . If a bilinear form B is symmetric, then it defines a quadratic form Q : V —•> K by the formula Q(v) = B(v,v). If the characteristic of K is not 2, then Q determines B by the formula B(v, w) = ± (Q(v + w) - Q(v) - Q(w)). Because B is symmetric, rjj = £j] : V —> l / v . I denote this map by Q :V ^ Vv. I denote rad(y, B) by r a d Q . In the context of quadratic forms, r a d Q is also called the kernel of Q and the vertex of Q. Let F(V) be the projective space of lines in the vector space V. A quadric in F(V) is a variety defined by Q(v) = 0 for a quadratic form Q. It is conventional to use the notation Q for both the quadric and the quadratic form. The quadric is degenerate if rad Q ^ 0 and nondegenerate otherwise. Every line v in rad Q is in this quadric. In particular, the quadratic form Q : V/ rad Q —> K is well-defined, and the quadratic form Q factors as V/vadQ For every short exact sequence of vector spaces 0 -» W -»• V -> V/W -> 0, there is a birational map TTW : P(V) —> P(V/W), the projection from the linear subspace W. It is defined on F(V) — F(W) because every line in V that is not a line in W maps to a line in V/W. The fibers of this projection are isomorphic to the affine space W. Using projection from the linear subspace radQ, a degenerate quadric Q is a cone over Q, i.e., the following diagram commutes and the vertical maps have fibers isomorphic to raclQ: Q - P(radQ) ^ n iadQ\Q F(V) - P(radQ) ^radQ -P(V/radQ). The quadrics are hypersurfaces, so their singularities are determined by the vanishing of the differential dQ — 2Q. Recall that the kernels of nonzero elements of Vv are hyperplanes in V, and the elements of V v may be used to denote the corresponding hyperplane in V. The tangent space to Q at a smooth point v is TVQ = Q(v) 7 because Q(v) is a hyperplane in V tangent to Q at v. Q(v)(v) = Q(v)=0. Therefore, the smooth locus of Q is Q — P ( r a d Q ) . In particular, the nondegenerate quadrics are smooth. 1.2 Restriction of Bilinear Forms and Rank Lemmas This section studies how the rank of a bilinear form changes when restricted to subspaces with emphasis on degenerate bilinear forms. The main result is Lemma 1.2.4. It is the key lemma in Theorem 3.3.6, which identifies the singularities of configuration hypersurfaces with a rank locus. The proof of Lemma 1.2.4 requires only the linear algebra of bilinear forms, so all the prerequisites have already been covered in Section 1.1. The specifics are quite distinct from the topics of Section 3.3 where Theorem 3.3.6 is explained, which is why the proof of the lemma is provided in this section. The linear algebra can be somewhat dry, so I try to motivate it by preceding the proofs with some examples and the statement of the main lemma. A few definitions are required before the statement of the lemma. The first is a mild symmetry condition. Definition 1.2.1 (Reflexive Bilinear Form). A bilinear form B on a vector space V is reflexive if B(v\, i^) = 0 implies B(v2, t>i) = 0 for v\, t>2 £ V. This condition is sufficient, to imply rad^> W = rad^ W for every subspace W, which is a key feature that is needed in Lemma 1.2.4. Definition 1.2.2 (Complete Set of Hyperplanes). A set of hyperplanes {Hi,.. in V is complete if Pq=lHi V in this definition. ., H^} = 0; note that k may be greater than the dimension of Every hyperplane is the kernel of a linear form on V, and scaling the linear form does not change the kernel. Therefore, the hyperplanes in V are identified with the points of P(V V ). I mention this well-known fact so that the following proposition makes sense. Proposition 1.2.3 (Complete Equivalent to Spanning). A set of hyperplanes {Hi,. ..,Hk} spans V v if and only if it is complete. This fact is well-known, and spanning is probably the preferred adjective. I prefer the term complete to emphasize that I will use the intersection property. Proof Let {Hi,..., H^} be a set of hyperplanes in V. Consider the exact sequence of vector spaces k k O^^Hi^V^V/^Hi^O i=l i=l and its dual k \ V k v ->y ->(H^:v o-+\v/f)H{\ Note that the span of the Hi as linear forms is exactly ( V/ Hi=l Hi) • Therefore, spanning is equivalent to completeness. • Lemma 1.2.4 (Criterion to Bound the Rank). Suppose that B is a reflexive, degenerate bilinear form on V and k is a positive integer less than dim V. Let {Hi,..., be a complete set of hyperplanes in V. For a subset J C. {!,...,£}, Hj = njGjH3. If B\JJ is degenerate for all subsets J with \J\ < n — k, then ranky B < k. define H^} 9 I work in the generality of bilinear forms that are not necessarily symmetric, but the bilinear forms to which I will apply these results will be symmetric. In particular, the distinction between left and right that I make in this section can be safely ignored for the applications I have in mind. I have included the generality of nonsymmetric bilinear forms because it draws attention to some details of the linear algebra. Let W be a subspace of the vector space V, and let B be a bilinear form on V. The bilinear form restricts to W, and this section seeks to clarify how the rank of B on V relates to the rank of B on W. In particular, the rank of each is determined by the dimensions of the radicals rad^> V = rad#(V, B) and r a d ^ W = rad^>(W, B\\y), which fit into the following commutative diagram: 0 w rad^H^ V{Blw) . wy n (1.1) 0 rad/j> V There is an analogous diagram for the left radicals. The rows are left exact by the definition of the radical. There is no natural map between the radicals of V and W. Note the difference between the notation of the horizontal map W r{Blw) : WW which is the linear map induced by the bilinear form B\w on W, and the diagonal map W {rB)lW : VV, which is the linear map TQ restricted to the subspace W. Before I proceed to the necessary lemmas, I provide a sketch of the argument in the symmetric case, which may be sufficient for some readers. Then I will give a few examples that demonstrate the possibilities. The fundamental idea of this section and the key to proving Lemma 1.2.4 is 10 Corollary 1.2.24. It says that if B is a symmetric degenerate bilinear form on V, then its rank is generic in the sense that in every complete set of hyperplanes, there is a hyperplane H for which ranky B = rank// B. In fact, the symmetric condition can be weakened to rad/, V = rad/j V, but it cannot be entirely removed as shown by Example 1.2.8. In general, the rank on the hyperplane has well-known bounds r a n k y B > rank// B > ranky B — 2, which I derive in Corollaries 1.2.13 and 1.2.15. These ranks are determined by r a d i i and rad V, which have no natural map between them. The possibilities are: 1. rank// B — ranky B — 2 when dim rad H = dim rad V + 1, which occurs when r a d V $1 rad if; 2. rank// B = ranky B — 1 when dim rad H — dim rad V, which occurs when rad H = rad V or both H n rad V £ rad H and H n rad V £ r a d V ; 3. rank// B = ranky B when dim rad if = dim rad V — 1, which occurs when rad H £ rad V. If B is nondegenerate on V, then rad V = 0, which simplifies the possibilities to 1. rank// B = ranky B — 2 when dim rad if = 1; 2. rank// 5 = ranky B — I when rad H = 0. If B is degenerate on V, then the nonzero rad V will intersect a generic hyperplane properly. By generic, I mean that the the set of such hyperplanes is dense and open 11 in the space of all hyperplanes. In this case, the set {H\ radV C H} is a proper closed linear subvariety of P(V) V because rad V is nonzero. Therefore, a generic hyperplane H does not contain radV^, so H + rad V = V. Then rad if must be orthogonal to all of V and not just to H, which implies rad H C rad V. If the above containment rad if C rad V is not strict, then radV is contained in H. The hyperplane H generically does not contain radV, so the generic situtation is a strict containment of radicals, which corresponds to the last case: 3. rank^ B — ranky B when rad H £1 rad V. The point of Corollary 1.2.24 is that if one has enough hyperplanes (i.e., a complete set), then the generic situation where rank^ B = ranky B must hold for one of them. If it held for none of them, then rad V would be contained in all of the hyperplanes and hence contained in their intersection, which is zero. The remainder of this section goes through this argument slowly and methodically and generalizes to higher codimensions. For a set of vectors {v\,..., v^}, I will denote the span by (vi,..., v^). In the following examples, I restrict bilinear forms to the coordinate hyperplanes. There is nothing special about the coordinate hyperplanes beyond forming a complete set of hyperplanes. I use the coordinate hyperplanes for convenience of calculation. Example 1.2.5 (Generic Symmetric Matrix of Rank k). Let V = Kn with the standard basis {ei,. .., en}. Let {X\,..., Xn] be the dual basis of Vv. Consider the 12 symmetric bilinear form * = £*? i=l of rank k. As a matrix in the standard basis, B has Is on the diagonal in the first k diagonal entries and Os everywhere else: A o o ... 0 1 0 o\ ... 0 0 1 ... 1 0 o) Vo The radical rad(V, B) is (c^+i,.. ., en). Let Hi be the hyperplane spanned by all e?- with j ^ i so that Xj|//. = 0; more simply, Hi — KerXj. Then the matrices for B\jj. in the standard bases for H{ are the matrices obtained by deleting the ith row and column from the above matrix. In particular, for i < k, rank//. B = ranky B — 1 because the dimension of the space has decreased by 1 but the radical has not changed. For i > k, rank//. B = ranky B because both the dimension of the space and of the radical have decreased by 1. In the complete set of hyperplanes {Hi,... ,Hn}, there are n — k (so in par- ticular at least one) with rank//. B = ranky B. Similarly, in the set of coordinate subspaces of dimension fc, there is only one with rank k. This fact is generalized to arbitrary symmetric bilinear forms and arbitrary complete sets of hyperplanes in Corollary 1.2.24. In particular, the hyperplanes need not be the coordinate hyper- 13 planes in the basis for which B has the above form. Example 1.2.6 (Two-dimensonal Case). Consider V = K2 with the standard basis E = {ei,e2}. Let { X i , X 2 } be the dual basis of V v . Consider a generic bilinear form B = aX\ (g> Xi + bXi ® X2 + cX2 <8> X1 + d X 2 ® X 2 , with a, b,c,dE K. In the basis E1, £? is represented by the matrix \c d) ' Let H\ = K e r X i = (e 2 ), and H2 = (ei). Then 5 ^ = d X 2 <8> X 2 (It would be more precise to write X2\JJ, B\H2 an = a X i (8) X i . d ^IIF2 nere ' but I opt for a less cumbersome notation.) Suppose that B\JJ. is rank 0 for both hyperplanes Hj. Then a = o? = 0. The rank of £? on V is then 1. r a n k y J B = 0 if b = c = 0, 2. ranky 5 = 1 if exactly one of 6 or c is 0, and 3. ranky B = 2 if both 6 and c are nonzero. If B is symmetric, then b and c are equal and both zero or nonzero. In particular, if B is symmetric and B\jj. is degenerate, then B cannot be rank 1 on V. Corollary 1.2.24 explains this fact in general. The precise statement is that if ranky B > r a n k # B for all hyperplanes if in a complete set and B is symmetric, then B is nondegenerate. The symmetry condition can be relaxed to rad^ V — rad^> V. I also note two logically equivalent statements: 1. If B is symmetric and degenerate on V, then in every complete set of hyperplanes, there is at least one hyperplane H with ranky B = rank// B. 14 2. If r a n k y B > rank// B for all hyperplanes in a complete set and if B is degenerate, then B is not symmetric (in fact, rad^> V ^ rad^ V). In particular, determining the rank of a degenerate symmetric square matrix can be done by taking the maximum rank of one of the minors obtained by deleting the ith row and column; there is no need to consider minors with different rows and columns deleted. This must be well-known, but I do not know a reference. However, to understand the way that I use this result, the reader should not think of a degenerate symmetric matrix restricted to the coordinate hyperplanes. Later I study degenerate symmetric matrices restricted to complete sets of hyperplanes, but these hyperplanes will not be coordinate hyperplanes in general. In fact, there will usually be more hyperplanes in the complete set than the dimension of the vector space, so it will be impossible to choose them to be the coordinate hyperplanes. Example 1.2.7 (Three-dimensional Case, Unequal Radicals). Consider V = K with be the dual basis of V v . the standard basis E = {ei,e2,e3}. Let {X\,X2,X%} Consider the bilinear form B = X\ <g> X2 + 2X2 <S> X2- That is, B(ei,e2) = l B(e2,e2)=2 B(ei, ej) = 0 for all other i,j. As a matrix in the standard basis, B is represented by /o 1 o\ 0 2 y0 0 0 . 0/ This bilinear form has rad^ V = (2e\ — &2-,ei) and corank 2. an d radjj V = (ei,e^). It is rank 1 15 Consider the three coordinate hyperplanes H\ = (e 2 ,e 3 ) Hi = (ei,e 3 ) # 3 = (ei,e 2 ). In particular, X\\H X{\JJ. is 0 for each i. The bilinear form B\u is zero. Again, I should write Du •> AT2|JJ is 2A"2 ® AT2 because t I prefer to keep the notation to a minimum. As a matrix in the standard basis, B ' » . - ( : : ) • The left and right radicals on H\ agree: r a d L # i = r a d j R / / 1 = (e 3 ). Moreover, the radicals of H\ are subspaces of the radicals of V. The rank and corank of B on H\ are 1. In particular, the rank on H\ agrees with the rank on V, and the corank has decreased by 1. The bilinear form B\JJ is zero because A"2|# 1S z e r o - Now the rank has decreased by 1 and the corank has remained the same. The radicals are both equal to 7/ 2 , which is the same as the right radical of V. The left radical of V is neither a superspace nor a subspace of if2. The case of B\fj = X\ <g> X 2 + 2AT2 <g> X 2 is similar to the case B\H except that radj^ H3 y£ rad^ H3. In particular, the matrix of B\JJ„ in the standard basis is Therefore, radjj H3 — (e\) and rad^ H% = (2ei — e 2 ). The main fact the reader should take away from this example is that the rank of 16 the degenerate form B matched the rank of B restricted to at least one hyperplane in a complete set of hyperplanes. Corollary 1.2.24 establishes that this is always true if radjj> V = rad^ V. This example shows that the condition r a d ^ V = rad^ V is not necessary for that conclusion. The next example shows that the conclusion does not hold in general. A secondary point that will be used along the way is that rad^ H-t C rad^ V or rad_R V C r a d ^ Hi for each hyperplane (and similarly with lefts and rights switched). In this example with rad^ V = (2e\ — e2, e 3 ) and rad^> V = (e^, 63), one containment in each row must hold: r a d L Hi = (e 3 ) C (2ei - e 2 , e 3 ) radLH2 radLH3 (ei, e 3 ) £ rad f i Hi = (e 3 ) = ( e i , e 3 ) £ (2ei - e 2 , e 3 ) (ei,e 3 ) C mdRH2 = (ei,e 3 ) = (2ei - e 2 ) C (2ei - e 2 , e 3 ) (ei,e 3 ) £ radRH3 = (ei). I demonstrate that at least one of the two containments holds for each hyperplane in each of the following examples. Corollary 1.2.20 establishes that this holds in general and is a key element of the main lemma. Example 1.2.8 (Necessity of Equal Radicals). Again let V = K set of coordinate hyperplanes {Hi, H2, H3}. with the complete Consider the bilinear form B = Xi ® A"2 + X2 <8> A"2 + AT3 ® X\ on V. As matrices in the standard bases, B and its restrictions are /o 1 o\ £ = 0 1 0 (1 0 5 #2 Si # 1 V -f° °) ~\1 0 SI # 3 In this example, B has rank 2, rad^ V = (ei — e2), and r a d ^ V = (e 3 ). The bilinear 17 forms B\H and B\JJ„ are similar to the first example, although their radicals are not all contained in the radicals of V in this example. Namely, they are rank 1 and r a d L Hi n r a d L V = (e 3 ) D (ei - e 2 ) = 0 rad^> H\ = (e 3 ) = rad^ V r a d L H 3 = (ei - e 2 ) = r a d L V vadR H3 n r a d ^ V = (ei) n (e 3 ) = 0 For the other hyperplane, /f2> the bilinear form is rank 1 and radx H2 = (ei) ^ r a d L V = (ei - e 2 ) vadR H2 = (e 3 ) = r a d ^ K The key point of the example is that B is a degenerate bilinear form of rank 2 for which there is a set of complete hyperplanes on which the rank decreases. Corollary 1.2.24 shows that this cannot happen if rad^ V = rad^> V. Note the following containments of radicals, where (ei — e 2 ) = rad^ V and (e 3 ) = rad^> V: r a d L Hi = (e 3 ) £ (ei - e 2 ) (e 3 ) C r a d ^ Hi = (e 3 ) r a d L # 2 = <ei) £ (ei - e 2 ) (e 3 ) C r a d ^ H2 = (e 3 ) r a d L # 3 = (ei - e 2 ) C (ei - e 2 ) (e 3 ) £ radRHs = (ei). I give two more examples. The first gives a degenerate, nonsymmetric bilinear form of rank 2 on A"3 for which rad^ V = rad^> V, and the conclusion of Corollary 1.2.24 is demonstrated. Then Example 1.2.10 demonstrates the contrapositive: a bilinear form with rad^ V = ra.dR V whose rank decreases when restricted to each hyperplane in a complete set. Corollary 1.2.23 implies that such a form must be nondegenerate, which is clear in the example. Example 1.2.9 (Demonstration of Corollary 1.2.24). Consider the bilinear form B = X2 ® Xi - Xi <g> X2 + Xi <g> X3 - X3 <g> X1 on V = K 3 . As matrices in the standard bases, the bilinear form and its restrictions are B = 1 [-1 B\ H 2 0 0 0 0' B \HX = 0 Oj 0 -1 'o - 1 1 0 ,0 0, B\H3 = 1 0 On V, the bilinear form has rank two with radicals r a d ^ V = rad^ V = (e2 + 63). When restricted to H2 or H3, the form still has rank two, and the radicals are zero. When restricted to Hi, the bilinear form is 0. Note the following containments of radicals, where (e2 + 63) = rad^ V = rad# V: r a d L Hi = (e 2 , e 3 ) £ (e 2 + e 3 ) (e 2 + e 3 ) C rad f l Hi = (e 2 , e 3 ) rad^ F 2 = 0 C (e 2 + e 3 ) (e 2 + e 3 ) ^ r a d ^ # 2 = 0 r a d L H3 = 0 C (e 2 + e 3 ) (e 2 + e 3 ) £ r a d ^ # 3 = 0. Example 1.2.10 (Demonstration of Corollary 1.2.23). Consider the bilinear form B = Xi <g> X3 + X3 <g> Xi + X2 ® X 2 on V = /C . As matrices in the standard bases, the bilinear form and its restrictions 19 are /o o i\ B = O 1 0 I#1 0 0/ \l B\ H 2 5 -~\1( ° 0A 5 #3 The bilinear form Z? is symmetric, so there is no need to consider the left and right radicals separately; denote either radical on a subspace W by r a d W . the hyperplane H2, B is nondegenerate, so r a n k ^ 2 B = 2 and r a d i / 2 = On 0. On the hyperplanes H\ and H3, B is degenerate of rank 1, r a d i f i = (63), and rad-H^ = {e\). On V, the bilinear form is nondegenerate, so ranky 5 = 3. In this case, r a d V = 0 is contained in all r a d f / j . In this example, notice that rankjy. B < ranky B for each hyperplane Hj. In fact under the condition rad^ V = rad/j> V, the inequalities rank/j. B < ranky B are a necessary and sufficient condition for B to be nondegenerate on V. The inequality is necessary for nondegeneracy because if ranky B = rank#\ B < dim Hi = dim V — 1, then r a n k y B < dim V. I prove sufficiency in Corollary 1.2.23. Example 1.2.8 shows that the inequality without the condition rad^> V = rad^ V does not imply this nondegeneracy. I now proceed to prove the lemmas before the reader starts to wonder whether I will resort to proof by example. L e m m a 1.2.11. Let B be a bilinear form on a vector space V, and let W be a subspace of V. Then W n r a d # V C rad f i W, and similarly for left radicals. 20 Proof. Consider an element w £ W^flrad^ V. As an element of rad/j V, B(v, w) = 0 for all v e V and, in particular, for all v G W C V. Because w is in W, this proves that w G rad/j W. Alternatively, note that W D rad^> V is the kernel of the diagonal map, (r^)\\y, in the diagram 1.1. That this kernel is a subspace of r a d ^ W is a consequence of the commutativity of the diagram. • In particular, the diagram (1.1) can be extended: 0 W nradjiV n •1.2) rad# W r[Blw \wv ^w n 0 rad^> V In fact, there are three exact sequences that fit into the following commutative diagram (note the middle sequence is only exact at the left and middle): radftW W n radR V W W r{Dlw) (n?)l w {V/{Wnrad Vj) L n rad/j> V V • (W/ rad L W)w n r-B (V/radLV)" v 21 I also record the following useful corollary: Corollary 1.2.12. / / rad^> V C W, then radjj- V C rad^VF, and similarly for left radicals. Proof. The assumption implies W n rad^> V = r a d ^ V , so Lemma 1.2.11 proves r a d # V C r a d ^ W. D Lemma 1.2.11 is a reformulation of the well-known fact that the rank of a bilinear form cannot increase when restricted to a subspace. In other words: Corollary 1.2.13 (Rank Upper Bound on a Subspace). Let B be a bilinear form on a vector space V, and let W be a subspace of V. with equality if and only if radjj V — rad^> W, rad^ V = Tadi Then r&nky B > rank^- B or equivalently with left radicals: W. Corollary 1.2.13 is well-known, but I still show how it can be deduced from Lemma 1.2.11. It follows from another familiar lemma from linear algebra: L e m m a 1.2.14 (Intersecting with a Codimension k Subspace). Suppose U\ and U2 are subspaces of a vector space V. Then dim U\ n U2 > dim U\ + dim U2 — dim V. In terms of codimension, this inequality is codim U\ n U2 < codim U\ + codim U^. Mixing codimension and dimension, the inequality is dim U\ H U2 > dim U\ — codim U2, which can be summarized when intersecting as the dimension of a subspace may drop by at most k with a codimension k subspace. 22 Proof of Lemma 1.2.14- F ° r every pair of subspaces U\ and U2 in V, there is a diagonal map A:UlnU2^Ui®U2 u I—> (u, u) and a difference map D:Ui@U2^>Ui (ui,u2) + i-> u\ - U2QV u2, which fit into the exact sequence of vector spaces o -»• U\ n u2 ^ Ui © u2 -^ ?/i + c/2 ->• 0Taking dimensions in this exact sequence gives a lower bound on the dimension of the intersection: dim Uif]U2 = dim Ui®U2- dim([/i + c/2) > dim U\ + dim U2 — dim V. D Proof of Corollary 1.2.13. Taking the dimension in the Lemma 1.2.11 shows that corankiy B is bounded below by dimVY D r a d ^ V . Apply Lemma 1.2.14 to the subspaces W and rad/j> V to get coranki^ B > dim W (1 r a d # V > dim W — codim radjj> V = dim W — ranky B Gathering the W terms in this inequality gives rank^/ B < ranky B. The inequalities are equalities if and only if radjj> W = W l~l rad^; V = rad^> V, or similarly with left 23 radicals. • I also note the well-known lower bound on rankiy B: Corollary 1.2.15 (Rank Lower Bound on a Subspace). Suppose W is a subspace of V of codimension k. Then r a n k ^ B > v&nky B — 2k, with equality when rad/^ V C W and dim r a d ^ V = dim rad/^ W — k. In other words for the case of equality, ifvadji V = rad^ V, then B defines a nondegenerate bilinear form B on V/ r a d ^ V that has corank k when restricted to Wf rad^> V. If ranky B — 2k < 0, then zero is obviously a better lower bound, and equality cannot be achieved in the high codimension cases k > ^ r a n k y B. Proof. The image of rad^> W under {TB)\W must go to zero in the map Vv —> W/V by the commutativity of diagram (1.1), so rB(r&dRW) C (V/W)v C Vy. Therefore, the following left exact sequence gives bounds on the ranks: 0 -)• W n x&dR V -> r a d ^ W (rg),H/ (V/W)y. ) Namely, an upper bound on the corank of B on W is c o r a n k ^ B = dim r a d ^ W = dim W fl r a d ^ V + dim rR (rad/j W) dim(V/W)v < dim W n r a d # V + < dim r a d ^ V + d\m{V/W)v = coranky B + k with equality when r a d ^ V C W and r # ( r a d # W) = (V/W)v. the dimensions in the exact sequence show that rR(radR If rad^> V C W, then W) = (V/W)v if and only 24 if k = dim(^/HO v = dim r # (radR W) — dim r a d ^ W — dim W n rad/j> V — dim radjj W — dim r a d ^ V; that is, rad/j> V" has codimension k in r a d # W. Translating the corank bound to rank gives n — k — rankjy B < n — ranky B + k rankjy B > ranky B — 2k. D Definition 1.2.16 (Orthogonal Subspaces). Let S be a subset of V. Define the subspace right orthogonal to S, ±] R(S) = Pi Kerr s ( s ) = lv G V\B(s,v) = 0 for all s e 5 } . There is a similar definition for the subspace left orthogonal to S, ±l(S). The dependence on the bilinear form B is implicit in the notation. I note the following properties to familiarize the reader with the definition. P r o p o s i t i o n 1.2.17 (Properties of Orthogonal Subspaces). The following are satisfied for right orthogonal subspaces. orthogonal There are similar properties for left subspace. 1. rad^> V C _L^(5) for all subsets S. 2. For a single element s E S, -L)j(s) = Ker r_g(s). 3- -l£(S) = rW-Lg(*)- properties 25 I mdRv = ±l(v) = nveV±l(v). 5. B is reflexive if and only if ±^(v) = ±^(v),for all v <EV. The proof is straightforward and will not be given. If S is a subset of a subspace W C V, then the subspace of W right orthogonal to S is ±%(S) = nseS Ker r ( B | )(s) = {w G VF | £ ( s , iu) = 0 for all s e S}. In this case, i_jji (£*) is a subspace of l-^(S) simply because W is a subspace of V. If the subset S is rad^ W, then the definition of the left radical shows W = ±%(radLW). W C ±^(radLW). Therefore, At this point, the formulas have begun to involve both right and left spaces. I did not assume symmetry of the bilinear forms so that the role of right and left in these formulas would be clear. Lemma 1.2.18 (Subspace Orthogonal to a Subspace Radical). Let B be a bilinear form on V, and let W be a subspace ofV. W + r8idR V C ±£(rad L W), and similarly with rights and lefts switched. Proof. As noted above, W C T^(rad L W) and rad^ V C 1^{S) Therefore, the sum is also contained in ±n(rad^ W). for every subset S. • Corollary 1.2.19 (Criterion for Radical Containment). If W + radjj> V = V, then rad^ W C rad^ V, and similarly with rights and lefts switched. 26 Proof. By the assumption and Lemma 1.2.18, V = W + r a d # V C ± ^ ( r a d L W) C V, s o V = -LjMradj^ W). By definition of right orthogonal, every element w £ rad^ W and every element v £ V satisfy B(w,v) = 0, which is the definition for w to be an element of rad^r V. • Corollary 1.2.20 (Hyperplane Radicals Contain or are Contained). If H is a hyperplane in V, then r a d # V C r a d ^ H or rad^ H C racl^ V-, and similarly with rights and lefts switched. Proof. By the hyperplane assumption, if rad/j>V ^ H, then radj^ V + H = V. Therefore, either (a) r a d # V C H or (b) if £ r a d ^ V + if = V. In case (a), Corollary 1.2.12 implies that r a d # V C r a d ^ f f . In case (b), Corollary 1.2.19 implies that rad£ H C rad^ V. • L e m m a 1.2.21 (Corank Criterion for Nondegeneracy). Suppose that B is a bilinear form on V with rad^ V = rad^> V. If coranky B < coranky B for every hyperplane in a complete set, then coranky B = 0. In other words, if the corank of such a bilinear form does not increase on any hyperplane in a complete set, then the bilinear form is Proof. Let {Hi,..., nondegenerate. Hi} be a complete set of hyperplanes for which coranky B < coranky. B for a l H € {1, ...,£}. In other words, the dimension of the radicals satisfy dim rad^ V < dim rad^ Hj, 27 so it is impossible for rad^ H-i £ rad^ V. Let / be the subset of { 1 , . . . , £} for which rad^ Hi = rad^ V. If i £ I, then rad^ Hi ^ r a d ^ V , so r a d # V C r a d ^ Ht by Corollary 1.2.20. Therefore, using the assumption rad^ V = rad/j> V, ™dLV C I f | r a d L i f i ) f| ( f l r a d f i i / i ] C f| ^ = 0. A logically identical proof can be given switching L and R. • The interaction of the left and right radical in the previous proof is the reason that I chose to include nonsymmetric forms. The condition rad/j> V = rad^ V imposes a mild form of symmetry on the form and is clearly satisfied by symmetric forms. Note, however, that this condition need not hold on a subspace of V. It may seem like Lemma 1.2.21 should be true without this condition because it is simply a statement about (co)ranks, which do not depend on left and right, but Example 1.2.8 shows that the lemma may fail without the condition. For comparison, Example 1.2.7 shows that it may hold without the condition. It will be useful to have the contrapositive statement. Corollary 1.2.22 (Corank Bound for Degenerate Forms). Suppose that B is a degenerate bilinear form on V with rad^ V = r a d ^ V . Then in every complete set of hyperplanes, there is at least one hyperplane H for which coranky B > 1 + corank JJ B. The reader may find the statement of Lemma 1.2.21 clearer in terms of rank. I stated it in terms of corank because the proof works with the radicals, so the proof is more direct in those terms. For the rank version, first note that Corollary 1.2.15 applied to a hyperplane H C V gives three possibilities for the rank of B on H: ranky B — 2 < r a n k ^ B < ranky B. The assumption of Lemma 1.2.21 rules out r a n k # B = ranky B. 28 Corollary 1.2.23 (Rank Criterion for Nondegeneracy). Suppose rad^ V = r a d ^ V. If ranky B — 1 > for all H in a complete set of hyperplanes, rankfjB then B is nondegenerate on V. That is, if the rank of B drops on every hyperplane in a complete set of hyperplanes, then B is nondegenerate on V. Note that the converse is straightforward by comparing dimensions: if B is nondegenerate on V (hence rank n), then its rank must drop on every hyperplane H because n — 1 is an upper bound for r a n k # B. Proof. The assumed inequality can be rewritten dim V — coranky B — 1 > dim V — 1 — corank^ B coranky B < corank^ B for all if in a complete set of hyperplanes, which is the same as the condition for Lemma 1.2.21. Hence, I make the same conclusion. • Again, the contrapositive stated in terms of rank will be useful. Corollary 1.2.24 (Hyperplane Rank Condition, B Degenerate). Suppose rad^ V = radjiV. If B is degenerate on V, then every complete set of hyperplanes has an element H for which r a n k y B = rank// B. Proof. The precise contrapositive is that r a n k y B < rank// B, but the inequality cannot be strict because the rank cannot increase when the form is restricted to a subspace (Corollary 1.2.13). • Corollary 1.2.24 says that under the assumption rad^ V = r a d ^ V, there is always a hyperplane in a complete set on which the rank of a degenerate form does not drop when restricted to it. In matrix terms, it says that a degenerate square matrix M with equal left and right radicals has the same rank as one cofactor matrix M ? ;J, i.e., the matrix obtained from M by deleting the ith. row and column. Because the 29 dimension of a hyperplane is one less than the dimension of the whole space, it is possible for the degenerate bilinear form to become nondegenerate when restricted to this hyperplane. Then the rank may drop when restricting to a codimension 2 space. However, it no longer suffices to suppose rad_j, V = rad^> V. I also need rad^ H = r&dftH for the hyperplane H in the conclusion of Corollary 1.2.24. For higher codimension, I will need to repeatedly make this hypothesis on each hyperplane in a flag. I simplify and assume B is reflexive. Corollary 1.2.25 (Existence of Nondegenerate Subspaces in a Complete Set). Suppose B is a reflexive bilinear form dim V > k > 0. Let {Hi,. on V. Suppose B has rank k on V and . . , Hg} be a complete set of hyperplanes in V. Then there is a set J C {1, . . ., £} such that 1. \J\=n — k, 2. dim if j = k, and 3. B\JJ is nondegenerate. Proof. Proceed by induction on coranky B (k < dim V, so the minimum corank is 1). If coranky B is 1, then the rank k is d i m V — 1. By Corollary 1.2.24, in every complete set of hyperplanes, there is an element H for which rank// B = k = dim H, so B is nondegenerate on H. Now suppose corank^ B = d is greater than 1. In particular, k = dim V — d < dim V — 1. By Corollary 1.2.24, there is a hyperplane Hi C V from the complete set for which rank//. B = k. Therefore, corank//. B = (dim V — 1) — k = (dimV= d- 1. l)-(dimV-d) 30 Because d > 1, A; is strictly smaller than dim Hi = dim V — 1. Thus, we can apply the inductive hypothesis to Hi and the complete set of hyperplanes in Hi {HjHHilHi^Hj} to conclude that there is a dimension k subspace Hj = H (Hj n Hi) of Hi satisfying the conclusion of the corollary. The set J is contained in { 1 , . . . , £}—i. Let J = J U {«}, and note that H J= r\(HjnHi)= jeJ (p[HA nHt= f]H3=Hj. \jsJ ) J^J The A;-plane Hj is also a subspace of V, which finishes the inductive step. • The contrapositive of Corollary 1.2.25 is Corollary 1.2.26 (Nonexistence of Degenerate Subspaces in a Complete Set). Suppose that B is a reflexive, degenerate bilinear form on V and k is a positive integer less than dim V. Let {H\,..., Hg} be a complete set of hyperplanes in V. If B\JJ is degenerate for all subsets J with \J\ = n — m and dim Hj = m, then ranky B ^ m. In particular, if the hypothesis is true for all m > k, then ranky B < k. The conclusion that ranky B < k cannot be made by assuming the hypothesis for k = m alone as the following example shows. Example 1.2.27. Let V — K 3 with the standard basis E = {e\, e2, 63}. Consider the 31 dual basis {X\, X2, X3} whose kernels define a complete set of hyperplanes Hi = Ke2 ® Ke3, H2 = Ke\ © Ke:i, H3 = Ke\ © Ke2. Consider the symmetric bilinear form B = X\ <g> X2 + X2 <g> X\. In the standard basis, a matrix representative is /o 1 o\ B = 1 0 0 \0 0 Oy The rank of B on V is two. In agreement with Corollary 1.2.26, taking J = {3}, B\u also has rank two. For all J C {1,2,3} with | J \ = 2 (i.e., the Hj are the coordinate axes), the bilinear forms B\}j are identically zero. In particular, when I J I = 2 and dim if j = 1, all the forms B\JJ are degenerate, but the rank of B on V is greater than one. From Corollary 1.2.26, the proof of Lemma 1.2.4 follows. Proof of Lemma 1.2.4- The assumptions of Lemma 1.2.4 imply the assumptions of Corollary 1.2.26 for all m > k, so the last comment of Corollary 1.2.26 proves the lemma. As an aside, note that there are more assumptions in Lemma 1.2.4 than are strictly necessary to get the assumptions of Corollary 1.2.26. Namely, B\JJ is assumed degenerate for | J \ < n — k, but Corollary 1.2.26 only needs to hold for those J for which 1. I J I = n — m < n — k, and 2. dim if j = m. • CHAPTER 2 FAMILIES OF BILINEAR FORMS The sections of this chapter recall two ways to construct families of bilinear forms. In Section 2.1, the degeneracy loci of a map of vector bundles are introduced. In Section 2.2, I describe quadratic linear series and their duals. This material may be well-known to experts, who will prefer to skip this chapter. The following chapters study specific examples of these families. In particular, configuration and graph hypersurfaces are degeneracy loci, and in special cases, they are duals to quadratic linear series. I focus on the simplest properties and examples, which suffice for the constructions in the following chapters. I warn and apologize to the reader that in the remaining chapters, I abuse sheaf and vector bundle notation. My motivation for abusing notation is that multiple vector spaces are involved at all times, and keeping them straight can be confusing. I choose the notation to make the vector spaces prominent. For example, for vector spaces V and W, I will use ¥(V) x W to denote both the trivial vector bundle over F(V) with fiber W and its sheaf of sections. Similarly, I use Of>/y\( — \) to denote both the tautological subbundle of the trivial bundle F(V) x V and its sheaf of sections. I try to indicate which interpretation I mean by the context. 2.1 Degeneracy Loci Let V, W, and U be vector spaces. Suppose a : V -> Hom(W, U) 32 33 is a linear map. The points of V can be partitioned by the rank of their image in Hom(VF, U). For v EV rank of a(kv). and a nonzero scalar k, the rank of a{y) is the same as the Therefore, it is natural to partition F(V) by this rank, as well. In fact, a defines a map of vector bundles: Onv)(-l) ® (F(V) x W) a F(V) x U F{V) The fiber of 0 ,F(vy •1) ® (F(V) x W) over a point L G 1P(V) (i.e., a line L in V) is L ® VF. Fiberwise, the map of vector bundles takes v ® w G L <8> H-7 to cr(^)(iu) G (7. There is a duality H o m 0 p ( l / ) ( 0 P ( y ) ( - l ) ® W, /7) - Rom0nv)(W, 0 P ( y ) ( l ) ® C/) where I have used W and U to denote the corresponding trivial bundles. Thus, the map a may also be considered with values in the linear forms on V. In particular, picking bases of U and W, one may think of this family as a dim U x dim W matrix with entries that are linear forms on V. This simple situation with U = W and a symmetry condition suffices for the applications in Chapters 3 and 4. However, I will mention the general definition: Definition 2.1.1 (Degeneracy Locus). Let X be a projective variety and let £ and T be vector bundles over X. Suppose a : £ —> T is a map of vector bundles. The degeneracy locus (of rank k associated to a) is Dk(X, It is standard to endow D^{X) a) = {x G X | r a n k e d < fc}. with the scheme structure given locally by the ideal of (k + 1) x (k + 1) minors of a, though I will not have much to say about the scheme structure. 34 If X or a is clear from context, I may omit it from the notation. Example 2.1.2. Let V = Hom(W/, U). Consider X = F(V), i.e., a generic projective space of linear transformations. Let £ = X x W and J7 = (X x U) ® O j ( l ) . There is a tautological vector bundle map whose degeneracy loci D^{X) are the (projective) set of all linear transformations from W to U with rank at most k. In particular, each bundle Dji(X) x W has a tautological map of vector bundles of rank at most k to{Dk(X)xU)®0Dk(x){l). Definition 2.1.3 (Determinant). Let a : V —> Hom(W, W) be a linear map of vector spaces. Let m be the dimension of W. Then D m _ i (X) is a hypersurface defined by the determinant of a matrix of linear forms on V representing a , a global section of Op/y\(m). The determinant of a, deter, is the ideal defining this hypersurface. I will also use det a to denote one of the generators of det a. The dependence on a choice of basis leaves some ambiguity in this notation. The above constructions also work for more specific types of linear transformations where rank makes sense. For a linear transformation a : V —> Sym W^, there is an analogous map of vector bundles and degeneracy loci in F(V). This example is central in Chapters 3 and 4. Example 2.1.4. Let X = P(Sym 2 Wy), £ = X x (W ® W), and T = Ox(l)- There is a tautological bilinear map of vector bundles X and the degeneracy loci of this map, called the generic symmetric degeneracy loci, are defined as in Definition 2.1.1. These degeneracy loci have been thoroughly studied. For comparison with the results of Section 3.3, I note the following theorem without proof. Example 22.31 in [Har92] gently introduces this result and proves part of it; full proofs can be found in [Ful98] or [JLP82]. 35 T h e o r e m 2.1.5 ( [JLP82]). Suppose the dimension of W is m. The m(m+l) of the generic symmetric degeneracy loci in P(Sym W ) = P codim Dm_k{a) If Qo £ -Cfc(cr) ~~ Dk-l(a)> = 2 codimensions 1 are . then QQ is a cone with vertex P(radQo) over a smooth quadric QQ in F(V/ r&dQo) (see Section 1.1). Then Dk(o~) is smooth at QQ, and the tangent space to Dk(a) at QQ is TQoDk(a) = {Qe More generally, if QQ £ Dk_i(a) Dk(a) | r a d Q 0 C Q}. — Dk_£_i(a), then the tangent cone to Dk(a) Qo is TCQoDk(a) and the multiplicity of Dk(a) = { Q e Dk(a) | r a n k Q | r a d Q o < £}, at QQ is m~k—l rm — k+£+a\ a=0 In the case k — m—\ V a ) and £ = j — 1, Equation (2.1) says that if = D(m-l)-(j-l)(a) = Dm-j((T) ~ - £>( m _i)_(j_i)_i(ff) D m-j-l(°~), then the multiplicity of QQ in the generic symmetric determinantal variety X at 36 Dm-i(cr) is the corank of QQ on W: Multo n 3C = Mult Q n £) m _i((T) m-(m-l)-l TT ^ n = n M^ a=0 /m-{m-l)+j-l+a\ m—(m—1) —a / /2a+l\ V a / ^ a=0 V a 7 =i This particular case of Theorem 2.1.5 will provide a useful comparison in Section 3.3, so I point out that the generality of Theorem 2.1.5 is not needed to see this simple result. Namely, if QQ has corank j , then there are coordinates in which where l m _ j j m _ j is the (TO — j) x (TO — j ) identity matrix, and 0X)y is the x x y zero matrix. In paticular, I may assume A\\ A\\ / = 1. In these local coordinates, X is defined by the determinant of 1 A12 \ ^ 0 and pick local coordinates such that A12 ... 1 + A22 ... Aim A2m ^l,m—j ± T -^•m—j,m—j ^m—j,m—j+1 ••• ^m—j,rn ^l.m—j+1 ^-m,—j,va—j+l ^m—j+l,m—j+l ••• -^m—j+l,m ^-m—j,m ^m—j+l,m ••• ^lm \ Amm J Therefore, the leading term of the polynomial defining X at QQ is the determinant 37 of the generic j x j symmetric matrix •™m—j+l,m—j+l ••• ^m—j+l,m ••• \ •™-m—j+l,m \ Amm J which has degree j . In Chapter 3, I will describe a projective linear subspace L of P(Sym W /V ) spanned by rank one quadratic forms. Corollary 3.3.10 will show that if QQ is in LC\X with corank j , then M u l t g 0 LHX =j. Because the multiplicity of QQ is the same in both X and Lf)X, the tangent cone in L n X is the linear section of the tangent cone in X: TCQoLr\X = LnTCQQX. In fact, L may be any subspace spanned by rank one quadratic forms as long as the radicals of the quadratic forms intersect at zero. More generally, if the radicals have a c/-dimensional kernel, then the multiplicity increases by d when intersecting with L. At this point, I note a few results on nondegeneracy and connectedness of degeneracy loci, which are well-known to experts but are not often cited in the literature on graph polynomials. T h e o r e m 2.1.6 (Fulton-Lazarsfeld [FL81]). Let £ v eg) T be an ample vector bundle on an irreducible complex projective variety X of dimension n. Suppose £ is rank e and T is rank f. For each section a of £ v <S> T', 1. if n > (e — k)(f — k), then D^(a) is nonempy, 2. if n > (e — k)(f — k), then D^{o) is connected. and 38 The theorem applies to the examples mentioned above because the trivial bundles tensored with 0^iy\(\) axe ample. The theorem may be refined under symmetry assumptions: T h e o r e m 2.1.7. Suppose A = (Sym £v) complex projective variety X of dimension (g> C is an ample vector bundle on a n where £ is a rank e vector bundle on X and £ is a line bundle on X. Let a be a section of A, • [IL99, Gra05] If n> e — k — 1, then D^{a) is • [Tu89, HT90] If n > ( e ~2 + 1 )> then D k(°) nonempty. is connected. Remark (1.8) in [FL81] notes that with further assumptions on Dfc(a), connectedness implies irreducibility using a theorem of Hartshorne [Har62]. In at least one simple yet important case that I will introduce in Example 3.1.5, a connected D}t(a) is not irreducible. This example is not nonsingular in codimension 1, a condition assumed in the cited remark. It would be interesting to find when the irreducibility conclusion can be drawn in the case of configuration or graph hypersurfaces. Lemma 3.1.10 provides a condition that implies reducibility for configuration hypersurfaces. 2.2 Quadratic Linear Series The point of this section is to recall the basic construction of a quadratic linear series and the corresponding map to projective space. In the case that the map to projective space is an embedding, I describe the dual variety and a resolution of its singularities. Most of the material could be developed easily without the quadratic assumption, but I focus on the quadratic case because that is all that I use in the following chapters. The idea to apply these constructions in Chapters 3 and 4 comes from [BEK06, Section 4], which is a suitable reference for the material of this section. A more detailed explanation of linear series can be found in most general algebraic geometry references, for example [Har77, Chapter II.7] or [Laz04, Chapter 1.1.B]. 39 Recall that the quadratic forms on a vector space W are naturally identified with the space of global sections r(P(wo,Op ( M O (2)). I abbreviate the notation for this space by T(0(2)) throughout this section. Definition 2.2.1 (Quadratic Linear Series). Let qo, . . . ,qn be quadratic forms on W. A direct sum L = ®?=0Kqi is a quadratic linear series. Every quadratic linear series comes with a linear map f:L-+T(0(2)), and the image of / is the span (qQ,..., qn) in the space of quadratic forms. If the qi are independent, then the map / is injective and the quadratic linear series is a subspace of Y{0{2)). Similarly, the dual map / v : T(C(2)) V - • L v will be surjective if the qi are independent. Letting U be the kernel of this map, there is the birational map, projection from F(U), 7Tfs/ : P ( r ( 0 ( 2 ) ) v ) - F(U) -+ F (L) v whose image is the projective space of lines in the image of / v . Recall that for a vector space V, every line in V (i.e, point in P(V V )) is an equivalence class of linear functionals differing by nonzero constant factors. These functionals all have the same 1. Most sources define a linear series to be a subspace of a space of global sections of a line bundle. Note t h a t the definition here may differ if the sections are linearly dependent. 40 kernel, a hyperplane in V, and conversely, every hyperplane defines a nonzero linear functional up to constant factor. These identifications allow the identification of the lines in \ / v with the hyperplanes in V, and it is conventional to use the notation P(Vy) or P ( V ) V for either space. Each point x of P ( W ) defines the subspace of sections vanishing at x in L or r ( C ( 2 ) ) . If there are sections that do not vanish at x, then the subspace of such sections is a hyperplane. Let B be the set of x for which qi{x) = 0 for all i, that is, n B = p | rad%. i=0 In linear series terminology, F(B) is the base locus of the linear series. There is a rational map QL:F(W)-*F(LV), regular on P ( W ) — F(B), which sends x to the hyperplane of sections vanishing at x. It is well-known and easy to show that there is a quadratic form on W not vanishing at x, so there is a regular map (in fact, an embedding) ¥(W) ^P(r(Q(2))v). Then Qi factors as P(W0-P(B) p(r(c(2)) v )-p(f/) Note that when F(B) is empty (i.e., B = 0), 7TJ-V is regular on i(F(W)) Ql is regular on P(H/"). In this case, the restriction of 7r^v to L(F(W)) because is affine and proper, hence finite. These maps can be defined in coordinates, and the quadratic linear series has 41 a natural basis of coordinates for this definition. The qi define a basis of L, and I denote the elements of the dual basis by Xqi. An equivalence class in P(X/V) may be represented in homogeneous coordinates as [OQ : • • • : an], which corresponds to the scalar multiples of the linear functional n In particular, the independence of the Xqi in L v implies that these linear functionals are nonzero as long as some aj is nonzero. In these coordinates, Qi is the map QL : P(W) - P(B) -> P(L) V [w] (->• [q0(w) : ••• : qn(w)]. In a basis for W, each quadratic form % corresponds to a symmetric k x k matrix Mi such that qi(w) = w MjW. The following proposition is proved in [BEK06, Section 4]: it is the main result of this section and is motivation for Section 4.4. I include its proof to prevent confusion with my notation, which differs from that of the reference [BEK06]. Proposition 2.2.2 (Dual of Quadratic Linear Series). Assume Qi is an embedding ofF(W) in P(L V ), and let X denote the image. The dual variety X^ is defined by det(^" = 0 v4jMj) = 0, where A{ are the coordinates on P(L). That is, it is the 2. The linear functional E iX Aw a q 1=1 may be zero for nonzero an if the base locus is nonempty, i.e., when the qt are not linearly independent in T(0(2)). 42 degeneracy locus Dn(o~) for the quadratic map of bundles a^ = \L) x °L (W®W) J2j/^icli: -onL)(i) P(L) Before proceeding to the proof, I explain some basic facts about dual varieties and some consequences in the case that the map Qi is an embedding. For a more thorough explanation of dual varieties and generalizations, see [GKZ94,Har92,Zak93, Kat73j. For a variety X C F(V), the dual variety Xv C P(V /V ) is defined as the closure of the set of hyperplanes that are tangent to X at a smooth point. More precisely, let Xsm denote the smooth points of X. Over Xsm. define YQ in F(V) x P ( V V ) as {(x,H)\xeX8m,HDTxX}. The incidence variety Yx is the closure,!^), and X=W2(Yx)=Pt2(Yo)i where pr2 is the second projection map in the following diagram: YQQYXQ F(V) x P(\/ v ) -£/•. ^v <? ^ Xsm^XC F(V) Pr2(r0)cx v cp(^ Note that YQ is a projective bundle over Xsm whose fiber over a point x is the projective space of hyperplanes containing TXX. That is, for x <E Xsm, p^1(x)nY0 = xxT(V/TxX)v, 43 which has dimension c o d i m X — 1. The fundamental theorem of dual varieties is T h e o r e m 2.2.3 (Biduality Theorem). For every closed, 'irreducible subvariety X in P(V), the incidence varieties are the same: YX=Yxv, and therefore ( X v ) v = X. Moreover, if x is a smooth point of X and H is a smooth point of X^, tangent to X at x if and only if x is tangent to X^ then H is at H. For the proof of the theorem, see [GKZ94] or [Har92]. If X is a hypersurface, then the map pr^ |y from Yx to X is birational because the generic fiber is a projective space of dimension 0, i.e., a point. By the identification Yx = ^ x v ' P r 2 \YY *S a ^ s o birational if Xv is a hypersurface. In the case that X is smooth, ^0 = YX, and Yx is a projective bundle over X, hence smooth. If X is smooth and X v is a hypersurface, then Yx = ^ v is smooth and birational to X^, i.e., a resolution of singularies. That is the key point: T h e o r e m 2.2.4 (Resolution of Dual Hypersurface). If a hypersurface is the dual Xv of a smooth variety X, then the incidence variety Yx is a resolution of singularities v forX . Assume Proposition 2.2.2 for the moment. Then Theorem 2.2.4 implies Corollary 2.2.5 (Proposition 4.2 of [BEK06]). Suppose that Qi is an embedding. Let X denote the image of QLlarities for X v = V(det(£ The incidence variety Yx is a resolution of singu- A.M,)). 44 In case the forms qi are all rank 1 and the base locus is empty, the polynomial ^(A) = det(^yljMj) is known as a configuration polynomial, which will be explained in Section 3.1. If Qi is an embedding, the theory of dual varieties constructs a resolution of singularities for the configuration hypersurfaces defined by the configuration polynomial. In particular, this resolution of singularities given by the incidence variety might be a useful tool for studying graph hypersurfaces. In Section 4.4, I show, however, that Qi is not an embedding for most graphs. I now proceed to the proof of Proposition 2.2.2. The proof follows from an alternate description of Yx in the case when X is smooth. For a bundle £ on X, let ¥(£) denote the projective bundle of hyperplane sections of 8. Let J\fx/F(V) ^ e the normal bundle of X in P(V), and thus P(A/V/p/m) is its projective bundle of hyperplane sections. Lemma 2.2.6 (Proposition 4.1 [BEK06]). For a smooth subvariety X of¥(V), the incidence variety Yx is Yx = The fiber ofYx ¥{Mx/nv))- over a point H in P(l / V ), thought of as a hyperplane in V, is nMX/F(V))\H = Smg(Xr\H). A general treatment without the quadratic case in mind can be found in [Kat73, Section 3]. 3. Some authors prefer to include the hyperplane sections in the notation. For example, the notation in [Zak93] is PCA/"x/p(v)(-l)), and the notation in [Kat73] is P(TVV). T h e notation I present is also common. The key is that the fibers of the bundle are hyperplanes in the fiber of the normal bundle or, equivalent ly, linear functionals up to scalar on the fibers of the normal bundle. Proof. For a smooth X, there is the normal bundle sequence over 0 -• Tx -* ?p(y)lx -> NX/v{V) ~^ °- The dual sequence provides the inclusion Recall the Euler sequence for P(V): o -> onv) -+ (P(v) x v) ® onv)(i) -+ rnv) -> The first map defines a global section s e v ® v v = r(p(\o, (P(V) x v) ® o P(K) (i)) whose vanishing defines the projective tangent bundle: V(s) = P ( T P ( y ) ) c p ( y ) x P ( y v ) . In a basis t>j of V and its dual basis X^ of F v , s is the form The projection pr 2 : F(V) x P(V V ) -»• P(\/ v ) has three restrictions: n^x/nv)) ^ nTnv)\x)c- PCV)) 46 Suppose the hyperplane H has coordinates [ag : • • • : an] in ¥(V ), i.e., H corresponds to the scalar multiples of the linear form The vanishing of this form on V defines the fiber of try over H by the Euler sequence (2.4): nTnv))\H = V ( J > ^ ) x {H} C F(V) x P(F V ). The fiber of TIX restricts the fiber of ny to X: nTv(y)\x)\H = ( x n v E ^ i , ) ) x{H}cXx p(yv). Let i x ff be an element of this fiber. The normal bundle sequence (2.3) is exact, so x x H is in P(A/V/p(m) if and only if ^ 04X; is zero on T ^ , i.e., TX,xCKer(52aiXi) = H, which means that x x H is in Yx by the other definition of the incidence variety. Thus, Yx = n^x/nv))A hyperplane H contains the tangent space to X at x if and only if H fl X is singular at x\ otherwise, the intersection H fl X is transverse and thus smooth. • Proof of Proposition 2.2.2. Take X = ¥(W) embedded by QL into ¥(V) = P(L V ) in Lemma 2.2.6. In particular X Xj of V is a subvariety of P(L) VV = P(L). The basis in the proof of Lemma 2.2.6 becomes the basis qi of L in this context. Evaluating qi on an element a = 5Z a ^<ji °f Lv gives (ft (a) = aj. By definition of Ql, if a = QL(W), then qi(a) = qi(w), which can be written as the matrices M{ in a basis for W. Over a point £ in P(£), represented by q — Yl aiq% in L, the fiber of the incidence 47 variety Yx is Yx,e = P (-A6f/P(LV)) if = ( * n V ( ] T a^)sing) x {£} = v(YJWi\w) . V*—' x {£} C P(WO x {£}. / sing I use qi\w to emphasize the step at which I consider the fact that q{ is a quadratic form on W, at which point it makes sense to replace qi\w by the matrix Mj. Note that the singular locus of a quadric V{Ylai^i) m PfW) is the vertex of the quadric P(rad^ajAfj) (see Section 1.1). In particular, q = Y2cHqi i s an element of the dual X v if and only if q is degenerate on W, so V(det Q r ^ M , ) ) =IVCP(L). For a generic q in X v , the radical radg is one-dimensional, so the fiber of Yx over X v is generically a point. • To apply the previous results, it is necessary to know whether Qi is an embedding. When the base locus is empty and 2 ( d i m W - 1) > n, the following theorem of Fulton and Hansen shows that it suffices to know whether Ql is an immersion. Corollary 4.4.2 applies this theorem. The proof may be found in [Laz04, Theorem 3.4.1] or in the original paper [FH79]. T h e o r e m 2.2.7 ( [FH79]). Let X be a complete irreducible variety of dimension k, and let f : X —> Fn be an immersion. If 2k > n, then f is a closed embedding. CHAPTER 3 CONFIGURATIONS A based vector space is a pair of a vector space V and a preferred basis E, often abusively called the basis. The basis defines a family of symmetric bilinear forms on this vector space, and that family restricts to every subspace of the vector space. A configuration is a subspace of a based vector space; it is not based in general, i.e., it has no preferred basis. One example of a configuration is the first homology of a graph, which is a subspace of the based vector space over the edges of the graph. This example will be treated in detail in Chapter 4. It determines the graph polynomial and graph hypersurface I begin in Section 3.1 with the description of the family of bilinear forms and the configuration polynomial, whose vanishing determines the locus of degenerate forms in this family. Two natural operations on a configuration are restriction and projection, which I describe in Section 3.2. These operations are reflected by the configuration polynomial in simple ways, and there are corresponding operations on graphs that reflect these operations, which I will discuss in the following chapter. The operation of restriction is especially useful for understanding the singular locus of the configuration hypersurface, the hypersurface defined by the configuration polynomial. I use this restriction operation in Section 3.3 to prove the main theorem of this chapter, Theorem 3.3.6, which relates the singularities and the rank of the points on the configuration hypersurface in a precise way. In Section 3.4, I discuss some simple consequences of Theorem 3.3.6 by counting dimensions of subconfigurations. 48 49 3.1 Configuration Hypersurfaces Let K be a field, and let V be a finite dimensional vector space over K. that E = {e\,..., Suppose en} is a basis for V. There is a based vector space isomorphism from V, with preferred basis E, to K particular, I use V, (V, E), and K , the vector space generated by the set E. In to denote the same based vector space depending on what I would like the notation to emphasize. A general element v of V can be written as v = ^Ji=\viei f ° r unique V{ 6 K, i = 1 , . . . n; Vj is the coordinate of v along ej. I also write this as v = J2eeEvee w h e n I would like to avoid unnecessary indexing. Each element e of the preferred basis E defines a linear functional Xe on K by assigning to v its coordinate along e: Xe : V ->• K Xe(v) = Ve. These elements also define symmetric rank one bilinear forms X£ on V: Xl : V x V -> K Xe (v, w) = Xe(v)Xe(w) = vewe. There is a natural map from V to the span of these forms inside the space of bilinear forms on V. I write this map as BE = Yl AeXe eeE :V "^ Sym2 VV a — i > V ] oeXe . eeE 1. A morphism of based vector spaces takes basis elements to basis elements or zero. 50 In the context of Section 2.1, BE is a map of vector bundles on ¥(V) F(V)x(V®V)^0¥{v)(l). There is also the corresponding map QE from V to the space of quadratic forms Y{Onv){2)) on V by letting QE(a)(v) The bilinear forms BE{a) = BE(a)(v,v). are represented by the diagonal matrices M,a,E [ a\ 0 0 a2 ••• 0 0 in the preferred basis E = {e\,..., o\ 0 an J e n } . Therefore, det BE — (^41^2 • •' A i ) 5 and the corresponding hypersurface in V or F(V) is the union of the coordinate hyperplanes (up to a choice of coordinates). Definition 3.1.1 (Configurations). A configuration W is a subspace of a based vector space. It is not assumed to be a based subspace. A generalized configuration is a linear map (p from a vector space W to a based vector space. In particular, for every generalized configuration <p, the image f(W) is a (nongeneralized) configuration in the based vector space. I study how the vector spaces of bilinear forms BE or quadratic forms QE behave when restricted to configurations. That is, each configuration W C V defines an inclusion of trivial vector bundles over P ( l / ) : P(V) x (W ® W) C P(V) x (V (8) V). I denote the composition of BE with this inclusion by BE\\y, QE\W r f ° composing the inclusion with and similarly I denote QE. Bear in mind that there may be no basis elements e G E in the configuration. However, some of the dual elements Xe will define nonzero linear functionals on 51 nonzero configurations, and therefore restricting Bj?(a) or Q_e(a) to a nonzero configuration defines a bilinear or quadratic form on the configuration that is nonzero for some a. If W is a configuration, then I will write X e | ^ , QE(a)\wi an d Bj?(a)\\y when I want to emphasize that I have restricted to W. I can also write BE(CL)\W as a w(a) matrix ^E m some basis of W. The choice of basis for W is suppressed from the notation. I reiterate that W is not a based vector space, so there is not a preferred basis in which to represent B^(a)\^/. matrix M^w^a) then ME w(a) The 7 is still symmetric but is not diagonal in general. If dimlT = £, is £ x £. As matrices with variable entries, the space of M g jy have entries linear in A\, . . . , An. I do not define or use the notation ME\W-I suggest using the diagonal n x n matrix ME = Mgy which might restricting to row and column vectors from W and not depending on a basis for W. Example 3.1.2. Let E = {^i,^2iez)i W7 be the configuration spanned by and ^ £\ = e\ + t2 and £2 = 2e% — e2- Then *i(h) = 1 xlih) = 1 Xl(£x) = 0 X*(£2)=0 Xl(£2) = 1 Xl(£2) = A Xf(£1,£2)=0 X22(£1,£2) = -l Xl(£1,£2)=0 Therefore, B^\y in this basis is A + A2[ V° 0/ -l\ (l 0\ ME,w = A1[ 1 V" /o ()\ + A3 !/ (Ai + A2 -A2 \ = 0 4 • ~M A2 + 4A3J 52 An element ME w(a) detMEwi0) °f the family ME W ^S degenerate if and only if = ( a l + G 2)( a 2 + 4a3) — a 2 = a\a2 + 4a\a^ + 4a 2 a3 = 0. Notice what happens if I write ME W m a different basis, say i\ — 2>t\ and £2 = £2. Then /9 ()\ \0 Oy / 9 -3\ V-3 W /0 ()\ V° V / 9 A i + 9^2 \ - 3 ^2 -3^2 ^2 + 4A3/ As explained for arbitrary bilinear forms in Section 1.1, the determinant of MEW differs from that of MWE by the square of the determinant of the change of basis matrix: det ME,w(a) = ( 9 a l + 9a 2 )(a 2 + 403) — 9a 2 = 9 a i a 2 + 36ai<23 + 36a 2 «3 = 9detMEjW. D e f i n i t i o n 3.1.3 (Configuration Hypersurface/Polynomial). Let W be a nonzero configuration in K . The configuration ideal of W is the homogeneous ideal de\,BE\wQK[Al,...,An}. The configuration hypersurface Xw of W is V(detE>E\W) tion -polynomial *i>w(A) is a generator of det BE\Wdet ME W m some element of K[Ai,..., — P(Vr) A configura- I n particular, the determinant basis for W is a configuration polynomial, and \I/jy(v4) is an AildimVF' the homogeneous polynomials of degree dim J47. The condition that W be nonzero is included to avoid ambiguity about the determinant in this case. In particular, the formula of Proposition 3.1.13 does not make sense for W = 0. T h a t formula is essential to many of the other proofs. I note, however, that defining ^\Y(A) to be constant if W = 0 is consistent with all the 53 following theorems about configurations polynomials by using some natural conventions. However, I have no use for the additional generality of allowing W to be zero, so I prefer to assume W is nonzero and avoid discussing the exceptional conventions for this degenerate case. Every configuration W C K is a configuration in multiple based vector spaces, for example by changing the preferred basis or including K in another based vec- tor space. The map of vector bundles that defines the configuration hypersurface depends on the preferred basis, so the configuration hypersurfaces do, as well. Therefore, it is important to understand that all statements about configurations are made with respect to a specific based vector space, which is usually implied and not explicitly mentioned or notated. When I need to keep track of the based vector space, I write the configuration as the pair (W, K ) . Remark 3.1.4. As described above, a preferred basis of V defines a map from V to v Sym V . For every subspace W C V, the natural surjective restriction map from Sym 2 Vv to Sym 2 Wv makes P(Sym 2 Vv) a cone over P(Sym 2 Ww) with vertex F{Z) where Z is the kernel of Sym 2 Vy —> Sym 2 Wv. rational map from F(V) to P(Sym 2 Therefore, the basis of V induces a v W ): n : P(V) - P(Z) n F(V) -* P(Sym 2 Wy). The image is a linear subspace of P(Sym 2 (3.1) Wv). The generic symmetric degeneracy loci in P(Sym 2 W /V ) are •£Sym,fc = Dk{a) = {xE P(Sym 2 Wv) | rank a < k}. Here a is the tautological map P ( S y m 2 i y v ) x (W®W) • Op(Sym2 P(Sym 2 Wy) wV)(l) 54 are Suppose dimW^ = m, so X s y m , m - 1 the points that are not full rank. In terms m(m+l) of matrices, P ( S y n r Ww) is the projectivization P 2 , of the space of m x m symmetric matrices, and 3£sym,fc *s the projective set of matrices of rank at most k. The map ix in (3.1) restricts to rational maps of configuration hypersurfaces m,m— 1' and there are similar statements about the other degeneracy loci. In particular, a point x of F(V) - F(Z) n F(V) is in Xw if and only if TT(X) is in X S y m i m _ x . If P ( Z ) and F(V) do not meet, then n embeds F(V) as a linear subspace of P(Sym 2 Wv). In this case, XSym^nF(V) = Dk(BE\w). Note that if the subspace F(V) is tangent to X g y m ^ at a smooth point of X g y m £, then that point will be a singular point in D^ (B^\^). Theorem 3.3.6 shows that this cannot happen for k ~ m — 1. Example 3.1.5 (Trivial Configuration). For the configuration W — K ready shown that tyw(A) = YieeE A e- , I have al- In particular, a configuration hypersurface need not be irreducible. Example 3.1.6 (Nontrivial Configuration). I computed two configuration polynomials in Example 3.1.2: $w(A) = A\A2 + ^A\A?> + 4 A 2 ^ 3 $w(A) = 9 A i ^ 2 + 36AiA3 + 36A2A^. I reiterate that I suppress the dependence of these polynomial on the choice of basis from the notation, and writing $f\y(A) does not specify a unique polynomial. Remark 3.1.7 (Generalized Configuration Polynomials). Suppose that <p : W —> K is a generalized configuration. The rank one bilinear forms X\ pullback to W by <p*X*(w) = X*(<p(w)). 55 The image of ip is a configuration in KE, 2 and the image of f* is a linear subspace V of Sym VK spanned by rank one quadratic forms. All of the following definitions and results about configurations and configurations polynomials apply to the configuration f(W) C K . In particular, a configuration polynomial for a generalized configuration could be defined as ty^ryy^A). The configuration hypersurface X<n(w) in P \KE\ would correspond to forms of rank diraf(W) rank dim W — 1 on W. The pullbacks f*X\ — 1 on p(W), not forms of will define degeneracy loci according to the rank on W, but K e r ^ is contained in all the radicals, so the maximum rank is dim ip(W). T h a t is, consider the subspace L of P(Sym 2 Wy) = P by f*Xg. Every element of L vanishes on Keif. In particular, r(r+l) 2 spanned L C P(Sym2(H//Ker^)V) C P(Sym2W/v). In terms of matrices, pick a basis {w\,.., ,wn} of W whose first k elements are a basis for Ker f. In this basis, Sym (W/ Ker f)^ corresponds to matrices of the form (° °) \0 MJ where M is a symmetric (n — k) x (n — k) matrix. Then L must be contained in the projectivization of this subspace. In what follows, all results are stated for configurations, configuration polynomials, and configuration hypersurface unless specifically stated for generalized configurations. The generalized situation does not apply to the applications in Chapter 4. The reader interested in the generalized situation can translate the configuration results to generalized configurations using the preceding comments. The following proposition shows that every subspace of Sym W^ spanned by rank one forms comes from a generalized configuration if : W —* K , and it is a configuration under a simple condition. P r o p o s i t i o n 3.1.8 (Constructing Configurations from Rank One Forms). Suppose {qi,. . ., qn} are a set of rank one quadratic forms on W. Let E be a set { e i , . . ., en}, 56 and let {X\,. .. ,Xn} be the basis dual to the basis E of K KE <p : W - • making a generalized configuration tion is a configuration . There is a linear map such that q{ = <p*X^. The generalized configura- when n f)mdql =0. i=l Proof. There exist linear forms i{ such that qi = if. Define a linear map n '• W Ke -" 0 i = RE n W i=. i=l Then the pulled back quadratic forms are (<p*X?)(w) = X?(<p(w)) ( n = il(wf = Qi(w). The generalized configuration is a configuration when ip is injective. The map if is injective if and only if P|Ker^ = 0. i=\ From the factorization q^ = if rad^j C Ker£j 57 because wEradqi => q~i(w,-) = 0 =^> Qi{w) = 0 ==^> £j{w) =0. Both radgj and Ker^j are hyperplanes, so containment implies equality. Remark • 3.1.9. The essential fact about rank one forms is that qi = if because the linear forms ^ are needed to define the linear map </?, which makes W a generalized configuration in K . L e m m a 3.1.10 (Direct Sum of Configurations). If W\ is a nonzero in K 1 and W2 is a nonzero configuration configuration in K 1 2 with configuration VW(A) = *Wiew2(A) in K 2 configuration , then W\ © W2 is a nonzero polynomial = VWl(A)VW2{A). As an application, I could have inductively computed Example 3.1.5, the trivial configuration polynomial corresponding to W = K . Proof. Select a basis for W = W\ © W2 consisting of a basis for W\ and a basis for W2. For e E Ei, Xe\\y2 = 0 and similarly for e E E2 and Wi, so in this basis, and therefore, the determinant of Bg\\y-.^Wo the BE\Wi. IS * n e P r °duct of the determinants of D A subspace W C V of dimension f is an element of the Grassmannian of £dimensional subspaces of V, Gr(£, V). When V has a preferred basis, the configuration polynomial for W is defined up to a constant, and you can compute a formula for the configuration polynomial tyyy(A) using the coordinates of the Pliicker embedding of GT(£, V) into P ( A ^ ) • The Pliicker embedding is the map that takes an ^-dimensional subspace W of V to the line det W = f\ W in f\ V. From an ordered 58 basis of V, denoted E = {e\,..., e I = e e en}, there is an induced basis on f\ V defined by r i\ A ' ' ' A i / f ° every set / = {?4,.. ., ip} such that zi < • • • < ig. Let Vj denote the subspace spanned by the e^ for i G / . By picking the basis J51, there is a projection 717 : V Vi eel In particular, det Vj is the line spanned by e j . The image of an £-plane W under the Pliicker embedding has a coordinate along each ej, and this set of coordinates is the set of Pliicker coordinates of W. For each I, write Pluckerj(W) for the coordinate of d e t W along I. That is, for an £dimensional subspace W of V, its coordinate Pliicker/(W) is the element of K such that det W —» det Vj is multiplication by Pliicker/(W): These coordinates are well-defined up to a constant representing a change of basis for W, and Pliicker/(W) is 0 if and only if W —*• Vj is not an isomorphism. A change of basis for V changes the basis vectors ej of / \ V, so the coordinates of W change. In the context of configurations, the based vector space K has a fixed preferred basis, so a general change of basis is not allowed. The preferred basis is not assumed to be ordered, and a change of basis of K by reordering may change the Pliicker coordinate for the index set I by ± 1 . The dependence of the coordinates on these choices of bases are suppressed from the notation. The preceding discussion simply explains that Pliicker coordinates are projective coordinates: L e m m a 3.1.11 (Pliicker Coordinates are Projective Coordinates). If p and 8 are 59 bases for W and Pliicker/ ^ ( W ) and Pliicker^ z(W) dinates, then there is a constant C, the determinant are the respective Plilcker coorof the change of basis matrix from P to j3, such that Plucker / ) / 3 (W) = C P l i i c k e r ^ W O for all It-subsets I of E, a basis for V. In particular, these coordinates are well-defined projective coordinates for the £-plane W. In terms of matrices, once I have chosen the basis for V, I can write a basis for W as £ row vectors in the coordinates of the basis for V. Arranging these row vectors into a £ x n matrix, the Pliicker coordinates of W are the £ x £ minors of this matrix. Example 3.1.12. Consider the configuration W of Example 3.1.2. In the ordered basis E = {ei, e2, 63} of K and the basis {£\, £2} of W, the matrix representing W is In the induced basis of f\ /l K 'l 1 0N .0 -1 2, , det W is l \ det W = det (l 0\ \ e\ A e^ + det \0 -l) / 1 0\ \-l 2) \ e\ A eq + det \0 2) e9 A ex = —e\ A e2 + 2e\ A e% + 2e2 A e$. Therefore, its Pliicker coordinates are [ — 1 : 2 : 2 ] . Notice the similarity between the last line and the configuration polynomial $w(A) = AlA2 + 4 ^ i A3 + 4A2A3. The next proposition explains this similarity. P r o p o s i t i o n 3.1.13 (Configuration Polynomial in Pliicker Coordinates). Let W be 60 a nonzero configuration in K-E . A configuration polynomial for W is VW(A) Yl Plucker F (W / ) 2 ]J FCE f£F F\=dimW = Af. Remark 3.1.14. The configuration polynomial is only denned up to a constant, so the ambiguity of which basis for W is used to compute Plucker^(W^) is irrelevant—it will still generate the principal ideal det BE\W. on the right side The basis for K is fixed up to order, which can only change the Pliicker coordinates by ± 1 . Therefore, the values Pliicker^?(W) are well-defined in the preferred basis up to a nonzero constant corresponding to a change of basis of W. The preferred basis removes any ambiguity about which Pliicker coordinates are used in the formula. Also, note that there is some nonzero Pliicker coordinate for every nonzero configuration W (i.e., the Pliicker embedding is a well-defined regular map, so not all projective coordinates may vanish), so the configuration polynomial is never identically zero. Proof. Pick a basis (3 = {wi,... ,wp} for W, and work with the matrix Mgj,y, which is the bilinear form B^\w written in this basis. As noted above, the entries in M^^w are l i n e a r in t n e ^ e , so the determinant is homogeneous of degree dim W. The bilinear form X\ has rank at most 1 on W. If it is rank 1, then there is a basis for W in which XJ is represented by a matrix with 0s everywhere except a 1 in the (1,1) entry, so (l 0 0 0 0 ME.W E — At A x fr feE-e \0 0 ... 0/ The variable Ae only appears in one entry of Mg^y, at most 1 in det Afg y/. If X | is rank 0, then M E,W AP0 feE~e and so it appears with degree 61 and it is still true that Ae appears with degree at most 1 in det Mp\y. This suffices to show that the monomials of det Mp \y are products of £ distinct variables, and hence there are constants cp such that detME^w= C F\\A!- J2 FcE feF \F\=dimW Evaluate both sides at Ae = 1 for e G F and Ae = 0 for e ^ F to determine the constants. Evaluating the right side gives cp. Evaluating detMpiy &ves the determinant of a bilinear form: det [ Y.X%V \ • JeF That is, cp = detY^f(=F^-'f\wbilinear form Xf\\y Y feF X j\w = With respect to the basis (3 = {w\,... is the matrix with i, j t h element XJ{WJ)XAWJ). Y X f(wi)Xf(wj) = (Xfk^) \Xfk^W^) = ,wi}, the Thus n F\w^F\w- / V/GF The matrix (Xf (WJ)) is the £ x £ matrix representing the map TIF\W :W ^ K F projecting W onto its F coordinates (so k is the row index, and j is the column index). Similarly, {XJAWJ)) is the matrix representing the transpose map *F\W- (KFY ^WV (so here k is the column index, and i is the row index). Taking determinants yields det Y feF X f\w = (detnF\w)2 = PhickerF(^)2. 62 • The formula of Proposition 3.1.13 will simplify the analysis of the singularities of the configuration hypersurface. First, I need to explain how it behaves under restriction. 3.2 Restrictions and Projections of Configurations Every based vector space K has natural based subspaces K for F C E. Moreover, the basis of a based vector space determines a canonical isomorphism with the dual space: e — i > Xe and extend linearly. Therefore, these based subspaces are also naturally based quotient spaces. For every subset F of E, there is a short exact sequence of based vector spaces 0 -»• KF -> KE 'nE-F) KE~F -»• 0. There is a natural splitting of this vector space from the isomorphism of these based vector spaces with their duals, and K is isomorphic to Kp. For a configuration W, there are two induced configurations in K tion W = K fl W and the projection Wp = np(W). : the restric- There is an induced exact sequence: 0 -> WF - • W ->• WE_F -> 0. There is not a natural splitting of this sequence. Note that Wp^p a subspace of W and W Both W is not generally is not generally isomorphic to Wp. and M ^ are subspaces of K , so they can also be considered configu- rations in K . Every configuration is a pair of a based vector space and a subspace, so ( W J and ( W (\Vp,K ,K J and (Wp,K notation, so I write W ,K J are two different configurations, and similarly for ) . I prefer to suppress the based vector space from the and Wp when I mean that the based vector space is K When I need to use the configurations (W , KE) the based vector space explicitly in the notation. and (Wp,Kj, . I will include 63 One reason for suppressing the based vector space is that the configuration polynomials are the same. For example, if W C KF C KE for some subset F C E, then the formulas for them given by Proposition 3.1.13 are V W ^ ) = S Plucker G (M/) 2 J ] Ag, GcF geG \G\=dimW and VW^) = £ Pl^kerG(VF)2 J ] Ag. GCE \G\=dimW geG There are two apparent differences between these two formulas: 1. There are more subsets G of E to sum over than subsets G of F. 2. ty,yr „E\{A) is a polynomial in more variables, namely the Ae for e G E — F. These apparent differences do not actually affect the polynomial: L e m m a 3 . 2 . 1 . Suppose W is a nonzero configuration every GCE in K and F C E. such that \ G | = dim W and G D (E — F) ^ 0, the Plucker For coordinate vanishes: Plucker G (W/) = 0. In particular, Proof. By assumption, Xe\w n G\w '• W ~^ K = 0 for all e € .E — F . Let e 6 Gn(E — F). Therefore, cannot hit the eth coordinate, so it is not a surjection and the Plucker coordinate is 0. • Remark 3.2.2. Note that this result corresponds to the fact that if W C K then det W is contained in / \ K , a linear subspace of / \ K . C K , 64 Example 3.2.3. I will continue with the configuration W of Example 3.1.2, whose configuration polynomial I computed to be VW(A) = AlM + 4 ^ i ^ 3 + 4A2A3. Let F = {e2,e%} C E. The basis {e\ + e2,2e3 — e2} of W maps to the basis {e2, 2e3 — e2} of Wp under the projection to K . In this basis for Wp, the bilinear form is the matrix M F,WF = A2^e2\wF + A 3^e3\\VF ( 1 -l\ /o 0N \-l 1/ VO 4, A2 -42 Therefore, a configuration polynomial for the projection configuration Wp is 1V F (i4) = A 2 (A 2 + 4A3) - A\ = 4A2A3. Notice that this is ^ ^ ( O , A2,A3). Now consider the restriction configuration W . In the basis 2e 3 — e2 for W the bilinear form A2Xk + A 3 Xi restricted from K is M „; W F = M ( l ) + A 3 (4) = [A2 + 4^l3 The corresponding configuration polynomial is ^wF(A) basis for WF, the bilinear form AiXf M £,(^,i^) = ^ + A2^2 (°) + ^ = A2 + 4^43. In the same + ^3-^f restricted from i ^ is ( l ) + ^3 (4) = [A2 + 4A3 65 Thus, ^^vF_KE^Ai,A2,A3) = ^wF{A2,A3). In fact, d — Vw(Ai,A2,A3) = *WF(A2,A3). For a subset F C E, let j4_p denote the set of Ae for e G F, so Ajr = 0 means that Ae = 0 for all e G F . Similarly, let <9^F, or simply <9f, be the derivative with respect to each Ae with e E F. The one well-known fact about projections that I will note is Lemma 3.2.4 (Projection of a Configuration). Let W be a configuration in K . If W = WQ, or equivalents WE'G = 0, then VW(A)\AE_G=O = *WG(A). Proof. The exact sequence 0 -> WE~G ->W->WG^>0 establishes the mentioned equivalence. Now set Api_G = 0 in the formula for ^iy(A) given by Proposition 3.1.13. Setting those variables to zero removes all terms that contain factors Ae for e G E — G. So ^W{A)\AF_G=Q IS o r u F F contained in G. For every F C G, the projection to K « I TVF\\Y W • W "G\W n W r > uWQ ^ G F > KK y a s u m over tne subsets factors as . In particular, Plucker^(H/) = det (nG\w) Pliicker F (WG). Therefore, the formula from Proposition 3.1.13 gives ^W{A)\AE_G=Q = det {TTG\W)2 ^WG(A). This suffices to prove the proposition because the configuration polynomial is only well-defined up to a constant. Note that if W = WG, • then W = WG because WG C W. However, even when W = WQ, the configurations are not equal in general, and WQ is not a subspace of W in general. I now provide a more detailed analysis of the restrictions, which play a prominent role in analyzing the singularities of configuration hypersurfaces. I start with the case of W for simplicity. By the general formula for configuration polynomials (Proposition 3.1.13), the configuration polynomial for a nonzero restriction W is ^wE-e{A) = Yl FcE-e \F\=dimWE~e For comparison, the partial of ^w(A) de^w{A)= Plucker F (wE~e)2 J ] Af. f£F with respect to Ae is Y, PliickerHWO2 I I F<zE | F | =dim W eeF A f- feF-e Using the bijection F — i > F — e between subsets of E of size dim W containing e and subsets F of E — e of size dim W — 1 de*w(A)= >T VmckeryUe(W)2l[Af FcE-e \F\=dimW~l This last polynomial differs from ^wE-e (3.2) feF f° r two possible reasons: 1. Pliicker^ Ue (WO ± Pliicker^ (wE~e in general, and 2. if W C KE~e, then VK = W ^ - 6 , and the sets F indexing the sum in have size dim W — 1, but the sets F indexing the sum in ^W^(A) de^w(A) have size 67 dim W. The following lemmas clarify these differences. Lemma 3.2.5 (Trivial Restriction, Simple Case). Suppose that W is a nonzero configuration in K 1. . The following conditions are equivalent. WCKE~e, 2. W = 3. VW(A) WE~e, = *wE-e(A), 4. PliickerF(WO = Plucker^ (wE~A for all F C E with | F | = dim W, 5. Pliicker^py) = 0 t / e 6 f , | F \ = dim W, 6. Ae does not appear in the polynomial 7. ^\y{A), de^W{A)=0. Proof. The following implications are straightforward: (1)^(2)^(3)^(4)^(5)^(6)^(7). Actually, assuming (2) W = WE~e really implies (3') ^\y — ^(u/E-e. KE\-> ^ u t ^y Lemma 3.2.1, ^ / w E - e j^E\ = ^\yE-eimplies (1). Note that P (/\eKE~e^j To complete the equivalence, I show that (5) is the linear subspace of P (/\eKE^j defined in the Pliicker coordinates by Pliicker^ = 0 for those F containing e. In particular, if Plucker F (H/) = 0 for all F containing e, then det W E P (f\e KE~e\. W C KE~e. Therefore, U The previous lemma generalizes by induction to subsets of E with more than one element. Lemma 3.2.6 (Trivial Restriction, Intermediate Case). Let W be a nonzero configuration in K . The following are equivalent: 1. there is a subset H C E such that W C K , 2. W = WH 3. *w(A) = *wH(A), 4. Plucker F (W) = Pliicker^ (\VH] for all F C E with \ F | = dim W, 5. PluckerF(WO = 0 tf F n ( F - H) ^ 0, | F | - dim W, 6. Ae does not appear m 7. deVw(A) ^\Y(A) = 0 for all for all e £ E — H, eeE-H. Proof. This is a straightforward induction on the size oiE—H from Lemma 3.2.5. • A further generalization that I use is: Lemma 3.2.7 (Trivial Restriction). Suppose W is a nonzero configuration in K . The following are equivalent: 1. there are subsets H C G C E such that WG C KH, 2. there are subsets H C G C E such that WG = WH, 3. ^wG(A) = ^wH(A), 4. Plucker F (\VG\ = Pliicker F (wH\ 5. Pliicker F (\YG\ = 0 if F n (G - H) ^ 0, | F | = dim WG, 6. Ae does not appear in 7. deWwG(A) ^WQ{A) = 0 for alleeG- for all F C E with \F\= dim WG, for all e G G — H, H. Proof. Apply Lemma 3.2.6 to the configuration W in KG. a 69 The case when W is not contained in K is described by formally differenti- $w(A)- ating L e m m a 3.2.8 (Nontrivial Restriction, Simple Case). Suppose W is configuration in K , and suppose that diiaW > 2 so that W cannot be zero and ^urE-e ts defined. The following conditions are equivalent: W£K E-e 1. 2. W e is a hyperplane in W, and there is a nonzero constant C such that for allF C E - e with \F\ = dimW E ~ e Pliicker^ (wE~e\ 3. de*W{A) = = CPlucker FUe (WO, *WE-e(A). Proof. (1) =>• (2): The dimension of a subspace drops by 1 when intersecting with a hyperplane that does not contain the subspace (Lemma 1.2.14). The projections •KF\wE-e:WE-e^KF have factorizations: KF\wE~e • W Extend a basis {w\, . . . , u> m -l} of W W K K • to a basis for W by adding an element wm. The matrix representing the map from W to K Ue in this basis for W and the canonical basis of K is <Xfl(wi) Xfl(w2) ... Xf2(wi) Xf2(w2) ... Xe(w2) ... \Xe(wi) (Xh(wi) Xf2(wi) V Here I let F — {f\,..., which has Xe(wm) Xfl{wm)\ Xf2(wm) Xe(wm)) Xh(w2) ... Xf2(w2) ... o Xh(wm)\ Xf2(wm) Xe{wm) o j / m _ i } . Expanding the above determinant in the last row as its only nonzero element gives Pliicker^WO = (-l)2mXe{wm) In particular, C = Xe(wm) Pliicker F (wE~e\ = Xe(wm) Plucker F (wE~e satisfies the conclusion of (2). The choice of wm can change the overall constant, but it is independent of the subsets F. (2) => (3): As noted in Equation (3.2), de*w(A) = ™&erpue(W)2 £ FcE-e F\=dimW~l H Af. feF By the assumption (2), the coefficients can be simplified to de^w{A) = C2 Plucker^ (\VE-e) Yl JJ Af. feF FcE-e F \=dim.W — l That is, de*w(A) = cHwE„e(A). There is an ambiguity about an overall constant on both sides because they are 71 configuration polynomials, so I can omit the nonzero C . (3) => (1): By assumption, de<S>w(A) = $wE_e(A). By Lemma 1.2.14, WE~e is nonzero because its dimension is at least one less than W. Thus, one of its Pliicker coordinates is nonzero, so de^\y(A) ^ 0 by the general formula for the configuration polynomial (Proposition 3.1.13). If W were contained in KE~e, then de^/\y(A) = 0 by Lemma 3.2.5, which would be a contradiction. Thus, W <£. K . • Corollary 3.2.9 (Isolating a Variable). IfW is a nonzero configuration in K that and We = 0 (i.e., e £ W), then is not contained in K VW{A) = AeVwE_e(A) + *WE_e(A). Proof. The two terms on the right side correspond to partitioning the subsets of E based on whether they contain e. The first term is determined by the preceding lemma using the assumption W <jt. K . The second term is determined by e Lemma 3.2.4 requiring the assumption W = 0. • Again, I generalize to larger subsets of E and higher derivatives. Lemma 3.2.10 (Nontrivial Restriction). Let W be a configuration in KE. H following conditions are equivalent for subsets H £ G C E assuming W The is nonzero: 1. for all sets H' with H £ H' C G and all e <E H' - H, WH' 2. W £ KH'~e, is a codimension \G — H\ subspace of W , and there is a nonzero con- stant C such that for all F C H with \F\ = dim WH, Plucker F (wH\ 3- d{G_H)^wG(A) = ^wH(A). = CPliicker^^.jy) (wG 72 Proof. Consider the configuration W in K and induct on the size of \G — H \. The case | G — H | = 1 is the content of Lemma 3.2.8. Now consider H C\ G with | G - H \ > 1. (1) => (2): Assuming the condition in (1) holds for H, then it also holds for H U e for every e E G — H because every H' that properly contains H U e also properly contains H , and every element of H' — H U e is also an element of H' — H. Therefore, the induction hypothesis can be applied to HUe to conclude that W is codimension I G — H — e | in j y e and there is a nonzero constant C\ such that Pliicker^ ( V f f U e ) = d P l u c k e r / , u ( G F _ e ) (FK G for all F <Z HUe with I F I = d i m V t ^ 0 6 . Condition (1) for H implies that WHUe <£ KH to Lemma 3.2.8 applied to the configuration W by taking H' = HUe. e in K Ue , W According is a hyperplane in VK-"Ue, and there is a nonzero constant C2 such that Plucker F (\VH) = C2 Pliicker F U e (w1 for all F C. H with | F | — dim W . Such F satisfy the conditions for F above. Therefore, the codimension of W in WG is 1 + \G — H — e | = | G — # |, and letting C = C1C2 gives Pliicker F ( l y ^ ) = C i C 7 2 P m c k e r F U e U ( G _ F _ e ) (\VG = CPluckerfu(G_F)(^G (2) =>• (3): In general, using the bijection between subsets of G containing G — H 73 of size dim W and subsets of H of size dimWG d(G-H)*WGW= Pliicker E -\G-H\, F{WG)2 II FCG \F\=dimWG G-HCF A f fep-{G-H) P l u c k e r ^ u ( G _ ^ (wGf ^ FCH =dim\VG-\G-H\ F E Pmcker — \G — H \ become F {WHf I I Af FCH p\=dimWH = Af. feF By assumption (2), the Pliicker coordinates and dim W d(G-H)*WG(A) = °2 J] feF C2*wH(A). Again, I invoke that configuration polynomials are only well-defined up to a constant to ignore the C . (3) => (1): By assumption, W" is nonzero, so one of its Pliicker coordinates is nonzero, and thus the general formula for t h e configuration polynomial, Proposition 3.1.13, shows that ^WH{A) is not identically zero. I prove by contradiction, so I will assume (1) fails and show that this implies that (3) fails. In particular, I will show that the failure of condition (1) implies 0(G-ff)*WG(^)=O, which contradicts condition (3) that 74 By assuming condition (1) fails, there are sets H' such that H ^H' <ZG and elements e G H' — H such that WH' £• ^ f l ' / - e i Let if be such a subset with the largest size, and let e G if — H be an element for which Either H = G or condition (1) is satisfied by H, i.e., for all if' with H £H' and all e G H' - QG H, WH' £ i^i/'"e. In case H = G, Lemma 3.2.5 implies that d^wG(A)=0, so 9(G-H)^WG(A) = d(G~H-e)de^WG(A) = 0. In case H c; G, the fact that i7 properly contains H implies that the induction hypothesis applies to H, and H satisfies condition (1), so by induction, condition (3) holds for H: Then apply Lemma 3.2.5 to W and e to find 75 Therefore, d{G-H)^WG(A) = d(&_-_H)d-ed{G_&)^wG{A) d (H-e-H)d^WH (A) 0. In either case, diQ_Fl\i$>wQ(A) = 0, but condition (3) assumes which is nonzero for a nonzero configuration W H D The following corollary summarizes the preceding results: Corollary 3.2.11 (Restrictions of Configuration Polynomials). Let W be a nonzero configuration in K dFmw(A) = . For all subsets F C E, VWE-F{A) ifWh~* tj t <^K-E-F'-e - " e for all F' £ F and all e £ F', otherwise. Conversely, if WE~F' dF*w(A) *WE-F{A) = < F' £F *WE-F'(A) = dE_F,Vw(A) £ KE~F'-e for all and all e e F', for some F' £ F. In particular, for every integer k, the following ideals in K[A] are the same dF*w(A) F\ <k *WE-F{A) F\<k 76 3.3 Singularities of Configuration Hypersurfaces This section describes the singularities of the configuration hypersurfaces in terms of their rank. I assume that the characteristic of the field K is zero. If the dimension of a configuration W is m, then the configuration hypersurface X\y is the degeneracy locus -Dm-1 (-£>£; IW-0 ^ f(K). The scheme structure of a degeneracy locus is defined by the vanishing of minors, so in this case, the configuration polynomial defines the scheme structure. There is a chain of degeneracy loci D0(BE\W)C C-.-C D^BEIW) C Dm-2(BE\w) C Dm-iiBslw) C Dm{BE\w) = F(KE). Definition 3.3.1 (Order of Singularity Ideal). Let X be a projective variety defined by a homogeneous ideal / in K[Ai,. S(I), . ., An]. The first order singularity ideal of /, is the ideal generated by the set { # — -}• The kth order singularity = 5(5(fc_1)(/)). ideal of / is S^(I) The phrase "fcth order singularity ideal" seems an unwieldy choice; the phrase "Jacobian ideal" is already taken [Rim72] and may have the wrong connotation as I discuss below. L e m m a 3.3.2 (First Order Singularity Ideal defines Singularities of Projective Hypersurface). Let X be a projective hypersurface. SingX = Then V{S(I)). Proof. In [Har77, Chapter 1.5], the singular points of an affine Y in A n of codimension r defined by fi, • • • , ft are shown to be defined by the typical rank condition on a 77 Jacobian matrix: rank ( ——- ) < r. dA3 If Y is an affine hypersurface, then this condition is that the rank of the Jacobian matrix is 0 at singular points y. T h a t is, the rank condition can be replaced by the condition that the matrix is identically 0. Therefore, the singular points of Y are defined by 8/ <v) = o BAj for all / G / (i.e., y is in the affine variety defined by S(I)). For a projective hypersurface A , take Y to be the affine cone over A and note that y G (SingF) — 0 if and only if [y] G Sing AT. Thus, S(I) also defines the singularities of the projective variety. • According to the lemma, the singularities of projective hypersurfaces are V(S(I)). However, Sing X will not be a hypersurface, so generally, V(S(2)(/))C_Sing(SmgA). In terms of the Jacobian matrix for Sing A , Sing(Sing A ) is defined by a rank condition, but V(S^ >(I)) corresponds to the Jacobian matrix being identically 0. Instead, the order of singularity ideals define the order of vanishing of the polynomials defining X in the following sense: Definition 3.3.3 (Order k Singularities). Let X be a projective variety defined by a homogeneous ideal I. The locus of order at least k singularities is the scheme defined by S^ '(I). As noted above, Sing>/e X ^ Sing(Sing A ._ 1 A"). In the case where A is a hypersurface, Lemma 3.3.2 states S i n g > 1 A = V(S(I)) = SmgA. of X, S i n g ^ A , 78 Let X be a projective hypersurface defined by a homogeneous polynomial / . If a = [a\ : • • • : an] is a point in X and a?; j^ 0, then the multivariate Taylor formula in the local coordinates with az- = 1 expresses / near a as A Al deg/ 1gJ/ m=0 | J^J = m J\ dAJ = Ev A a j Therefore, when a 6 Sing>^._^ X, all the terms with m < k are zero and A Al A ^, ,^ J m=fc J \=m A _ a \ A J! <9A (3.3) ^i i Then Sing>£_i X is the locus of points where / has multiplicity at least k, and the set of points of multiplicity fc, Mult/. X, is Multfc X = Sing >A ,_ 1 X - Sing>fc (3.4) Picking out the kth term of Formula (3.3) defines the affine tangent cone TCaV(f) 1 dJf =V A A ^ k J l d A J - a a \ A- (3.5) I use V to denote either affine or projective varieties, letting the context determine which one is meant; Equation (3.5) is affine. One may homogenize the leading term at a A^ Ja.k Al v 1 d 7f ' A - a A a.J A • n-' \J\=k 1 A i ojf E ^ UftkJ'dAJ J A_a(alA-aAl) ~^i a i , 79 so the projective tangent cone to V(f) at a is T, A = a_ A~ a{ |J|=fc The notation (a^A — aAj) {a,A - aAiY (3.6) for a tuple J = (JQ, ... ,jn) is shorthand for {atA - aAi)J = ]J {aiAe - aeAi)je. £=0 In particular, if ji > 0, the product is zero. Proposition 3.3.4 (Configuration Tangent Cone). LetW be a nonzero configuration in K . If a = [a\ : • • • : an] is a point of multiplicity k with a^ ^ 0, then the tangent cone to X]Y at a is \ TCaX\y E =V a\ *WE-J ( A A a <hj \ i JCE,\ J\=k \dim W7 —dim W—fc a J i The projective tangent cone to Xyy at a is ( TCaXw £ =V ^WE-J ( —1 (aiA~aA,y JCE, | J\=k Vdim WE~ J = d i m W-k J Proof. Proposition 3.3.4 follows from Equations (3.5) and (3.6) with / * W- Namely, the fcth order term of ty\y at a is V W.a,k A A £ J\=k 1 dJ$> w J! dAJ A A a - at A~i a u n\A~~M i The configuration polynomial has degree at most one in each variable (Proposition 3.1.13), so only tuples J that are sequences of 0s and Is need to be included 80 in the sum. Such tuples correspond to subsets of E. Corollary 3.2.11 simplifies the derivatives to the configuration polynomials ^WE-J • Note that if | J | = k but E dimW ~^ dj^w 7^ dimVF — k, then Corollary 3.2.11 shows that the partial derivative is identically zero. • Euler's formula for homogeneous polynomials states E A ~ = deg(/)/. (3.7) Therefore, each homogeneous / in / is also in S(I) using the assumption that the characteristic of K is zero so that deg(/) is invertible. The ideal / is homogeneous, so every g E I can be decomposed into its homogeneous pieces, and g is also in S(I) by applying the same reasoning to its homogenenous pieces. Therefore, the order of singularity ideals form a chain I CS(I) c sW(I) c ..., and so do the order k singular loci X D Sing>! X D Sing>2 X D .... In particular, note that the fcth order singularity ideal can be defined by grrif 5W(/) dAl1 •••dAJnnn n f G /, m < k, and N , 3i = i=\ \ m I Lemma 3.3.5. If I is generated by a set of homogeneous polynomials {f\,.. ., fj,}, then S(I) is generated by Proof. The set L is contained in S(I) by definition. First note that each fj is also in the ideal generated by L by Euler's formula 3.7. Consider a homogeneous element 81 f £ I. Then there are homogeneous polynomials h\,..., h^ such that k 3=1 Differentiating gives df__A(dh1 dfj_ Both terms on the right are in the ideal generated by L. • The main result of this section is T h e o r e m 3.3.6 (Singular Loci are Degeneracy Loci). Let W be a nonzero configuration of dimension m, in K and X\y its configuration hypersurface in ¥(K ). Then Sing>kxW = D m~k,-l(BE\w), for 1 < k < m — 1. In other words, the locus of points where tyyy has multiplicity least j is Dm_j(B^\^). The case k = \ states SingXvj/ = Remark at Dm_2(BE\w). 3.3.7. Consider the particular case k — 1, that is, the singularities of the degeneracy locus X\y coincide with the locus of next lower rank. This result is true for the generic determinantal varieties X C P(VKV <g) Wy) and X g v m c IP(Sym2 Wv), but the behavior is not true for all degeneracy loci. For example, intersecting the generic determinantal variety with a hyperplane H will cause Dm__2(Xn/f) CSingXntf. If the hyperplane H is tangent to X at a smooth point, then the inclusion is strict: there will be singular points of X D II that are rank m — 1. The set of hyperplanes that are tangent to X at a smooth point are contained in the dual variety of X, which 82 has positive codimension in the dual projective space. In particular, the set of such hyperplanes is not open nor dense in the set of all hyperplanes, so getting a strict inclusion is not generic. As discussed in Remark 3.1.4, the image of the X\y under a particular rational map 7T :F(V) --+P(Sym2W/V) is the intersection of a linear subspace with Xsym- Theorem 3.3.6 implies that this linear subspace cannot be tangent to Xsym at a smooth point. The proof does not take this geometric perspective, and it would be nice to have a more direct geometric understanding of why this particular linear subspace is not tangent to Xsym- A key feature seems to be that it is spanned by rank one forms. The method of proof that I present for Theorem 3.3.6 works for the generic determinantal variety. In particular, if D is the generic determinant defining X, then dj\.D is the minor given by removing the unique row and column containing A{. These all vanish at x if and only if x has rank at most m — 2. This argument generalizes to higher derivatives and other minors. In the case of the configuration hypersurface, the argument is more subtle. In particular, not all minors can be found as d^.^\y shows that dj\.^w for some A{. Corollary 3.2.11 are determinants of matrices, and Corollary 1.2.24 shows that if enough of these determinants vanish, then ty\y vanishes, as well. That is the basic argument, which I complete in detail below. The next lemma follows from the analysis of restricting configurations in Section 3.2. L e m m a 3.3.8 (Singularities Determined by Restricted Configurations). Let W be a nonzero configuration in K . Sing>fc Xw = V ((VWE-F \F\<kJj = P| FCE \F\<k X{WE-F^KE) 83 Proof. The lemma restates Corollary 3.2.11, which established the equality of the defining ideals. • Proof of Theorem 3.3.6. First note that the condition on k, k < m — 1, implies that dim WE~F = dim W D KE~F > m —k > m — (m — 1) = 1 for | F | < k by the usual bound on the dimension of the intersection (Lemma 1.2.14). In particular, W is nonzero, and its configuration polynomial is defined. By Lemma 3.3.8, it suffices to show that a point a is in V ((^TTT-E-F | I F | < k)) if and only if Bj^(a) has rank at most m — k — 1 on W. If BE (a) has rank at most m — k — 1 on W, then it has rank at most rn — k — 1 on a subspace WE'F (Corollary 1.2.13). If | F \ < /c, then ^\mWE~F (Lemma 1.2.14), so BE(CI) is not full rank when restricted to W > m-k . Therefore, the determinant det BE{O)\WE-F = %WE-F{a) = 0. If a E V {{'fyyyE-F | I F | < k)), then Bjr(a) is degenerate when restricted to all subspaces W for | F \ < k. Note that the subspaces W E e of the complete set of hyperplanes < W ~ are intersections E e | e E E, W ^ W ~ conditions of Lemma 1.2.4 are satisfied, so the rank of Bfi(a) >. Therefore, the is at most m — k + 1. • Note that dim F(V) = n - l > m - k - l for all nonnegative integers k because m < n. In particular, the hypotheses of the nonemptiness part of Theorem 2.1.7 are always satisfied, so the D^{W) are nonempty for all k between 1 and m—1. By Theorem 3.3.6, Sing>^ X^ is therefore nonempty for such k, as well. The hypotheses of the connectedness part of Theorem 2.1.7 are 84 more subtle, so using it to decide whether Sing>^ X\y is connected will depend on the specific values of n, m, and k. Example 3.3.9 (Trivial Configuration). The configuration W = K^ introduced in Example 3.1.5 has the union of the coordinate hyperplanes as its configuration hypersurface. For each k between 1 and m — 1, Sing>^ X\y is the union of the coordinate linear subspaces of codimension k. In particular, Sing>^. has codimension 1 in Sing>^._i. For example in K , there are three distinct coordinate hyperplanes whose union is X\y. These planes meet in three distinct lines whose union is SingXyj/. Corollary 3.3.10 (Multiplicity of Configuration Hypersurfaces). Suppose W is a nonzero configuration in K M\AtkXw . The set of points of multiplicity = Dm_k{BE\w) - k m X\y is Dm_k_i(BE\w). Proof. The corollary simply restates Equation (3.4) using Theorem 3.3.6: Mult*. Xw = Sing^fc.! Xw = Dm-k(BE\w) - Sing> fc ~ Xw D m-k-l(BE\w)- D Corollary 3.3.11 (Intersection of Tangent Cones). Let W be a nonzero in K . Let Z be the kernel of the restriction configuration map Sym2(iv^)V^Sym2^v. Let L = F(n(BE(A))) be the image of the family BE{A) X be the generic symmetric = KE in P(Sym 2 Wy). 2 v degeneracy locus in P ( S y m V y ) . Note that Xw cone over L n X with vertex V = F(KE) Multa; Xw fl F(Z). For every x G X\\r — V, = M u l t ^ ) X, Let is a 85 and therefore, TC7r{x)(Lnx) = LnJCn{x)x, and similarly for the affine tangent cones. When V = 0, n em beds F{KE) into P ( S y m 2 V ^ v ) ; and TCXXW Proof. When TCntx\X = LH TCXX. is cut by the linear section L, the leading term at x may vanish leading to a higher multiplicity. By Corollary 3.3.10 and Theorem 2.1.5, both Mult x X\y and Mult^^-j X are the corank of bilinear form on W, so the multiplicity does not increase on X-yy- Therefore, the leading term defining X\y at x is found by linear substitution of L into the leading term defining X at x, which means that the tangent cone of L n X is just the linear section. Remark • 3.3.12 (Generalized Configuration Hypersurfaces). If ip : W -» KE is a generalized configuration and dim Ker tp = d, then the results of this section still hold for the configuration (f(W) C K . The locus of corank k in (p(W) is the locus of corank k + d in W, so putting the formulas in terms of W instead of >p(W) is possible by modifying k appropriately. 3.4 Subconfigurations Let U and W be configurations in K defined by the basis for K with U C W. The bilinear forms By (a) restrict to both U and W. I write D^{U) and D^{W) for the corresponding degeneracy loci of forms of rank at most k. I use the convention that Dk(U) = 0 if k < 0 and Dk(U) = r(KE) if k > dimU, and similarly for W. This section deduces some simple relationships between the singularities of Xjj and X\Y by simply counting dimensions. 86 Suppose U is codimension £ in W. By Corollaries 1.2.13 and 1.2.15, a rank k form on W restricts to a form with rank between k — 2£ and k, so Dk(W) - Dk^(W) c Dk(U) - £>fc_2£-i(J7). In particular, and Dk_u_x{XJ) C Djfe.iCW). (3.8) Suppose dim W = m, so the configuration hypersurfaces are XW = D m _i(lV) Jfy = A„_,_i(E0- Note that XJJ and Xjy are not necessarily contained in one another. However, the containment Dm-i-i(W) C Dm_e^(U) can be restated as Sing>£ Xw C JQy using Theorem 3.3.6. More generally, e-k-i(W) c Dm_i_k_i(U) implies In particular, when [/ is a hyperplane in W (a case that will have applications for graph hypersurfaces), SingXpy C XJJ, and S^g>k+1Xw C Sing> fc X[/. 87 I have proved the following lemma: Lemma 3.4.1. Let U and W be configurations in K with U C W. Suppose that U has codimension £ in W. Then Sing> f c + ^X W C Sing>fc Xv. In particular, if U is a hyperplane in W', then SingXpfA C XJJ. The lemma follows directly from Dk{W) C Dk(U). The other containment (3.8) Dk-2l-l(U) C Dk_x(W) proves a similar lemma: Lemma 3.4.2. Let U be a codimension £ subspace of W and W a configuration in KE. For all k, Sing>fc+£X(7 C Sing>fcXVy. In particular, if U is a hyperplane in W, then SingXu C Xw. C Dk.^W) Proof. Replace k in (3.8) Dk-2e-l(U) by m + I — k to get Dm_t_k_x(!J) C Dm+i_k^(W). 88 By Theorem 3.3.6, Dm_e_k_i(U) = Sing> fc XJJ and D m + ^_ f c _l(Vl/) = Sing>fc_^ Xw Therefore, Smg>kXu C Sing>fc_^y. In particular, when U is a hyperplane in W (i.e., £ = 1), taking k — 1 gives SingX[/ C X ^ / . D Putting together the previous two lemmas proves the next theorem. T h e o r e m 3.4.3 (Relationship Between Subconfiguration Hypersurfaces). Let U be a codimension I subspace of W and W a configuration ( S i n g > f c + ^ X ^ ) U (Smg>k+iXu) In particular, if U is a hyperplane in W, (Sing Xw) in KE. C (Sing> fc A" w ) n For all k, (Smg>kXu). then U (Sing Xu) CXwnXu. Example 3.4.4. Consider the configuration W of Example 3.1.2 whose configuration polynomial I computed to be VW(A) = AlA2 + 4^iA3 + 4A2A3. The singular locus of its configuration hypersurface is Sing Xw = V(A2 + 4 ^ 3 , Ax + 4A3, AAi + 4A2) = 0. 89 This is a subconfiguration of the trivial configuration K . In accordance with Theorem 3.4.3, SmgXKE = {[1 : 0 : 0], [0 : 1 : 0], [0 : 0 : 1]} C Xw. CHAPTER 4 G R A P H HYPERSURFACES The study of configuration hypersurfaces in Chapter 3 was motivated by the example of graph hypersurfaces, which I present in this chapter. Section 4.1 introduces graphs from both topological and combinatorial perspectives. The material is necessary for the following sections but is elementary and well-known. In Section 4.2, I discuss how the homology of graphs defines configurations. In particular, I derive the usual combinatorial formulas for the graph polynomials as configuration polynomials. The results about configuration hypersurfaces in Sections 3.3 and 3.4 apply to the case of graph hypersurfaces, and I restate them here. In Section 4.3, I recall how some basic graph operations are reflected in the graph polynomial and graph hypersurface. I conclude in Section 4.4 with the proof that the quadratic linear series defined by the graph is not an embedding for most graphs. 4.1 Graph Preliminaries The material in this section is well-known graph theory and algebraic topology; for more details, see [Mas91, Section VIII.3] for the algebraic topology and [Lov93] for the graph theory. I use the following definition of graph. Definition 4.1.1 (Graph). A graph G is a finite cell complex consisting of 0 and 1-cells. The 0-cells of G are its vertices, denoted V or V(G). edges, denoted E or E(G). The 1-cells of G are its To avoid trivialities, I assume V ^ 0. A typical example of a graph is shown in Figure 4.1. There are many definitions of graph in the literature, and Definition 4.1.1 includes many of these definitions as special cases. I emphasize that the generality of Definition 4.1.1 does not include 90 91 Figure 4.1: A typical graph. graphs with an infinite set of edges or vertices. Otherwise, Definition 4.1.1 is quite general and includes graphs underlying Feynman diagrams (although I do not distinguish between internal and external edges, but this feature could easily be included by partitioning the set of edges). A more traditional, graph-theoretic definition is that a graph is a set of vertices and a set of edges where each edge is a set of two vertices. This definition fits into Definition 4.1.1, but Definition 4.1.1 is more general. In particular, multiple edges may connect the same pair of vertices, and an edge may connect a vertex to itself. One may redefine graphs in Definition 4.1.1 as a set of vertices and a multiset of edges where each edge is a 2-multiset of vertices; this definition corresponds to a multigraph or pseudograph in the graph theory literature. The homology of a graph G is its homology in the usual topological sense. As a cell complex, its homology may be computed using cellular homology. In particular, the homology of G is the kernel and cokernel of a map d : If -)• Z 92 defined as follows. For each edge e G E(G), the gluing map from e to the vertices of G is defined by mapping the boundary of e to two elements of V(G) (not necessarily distinct). Denote these elements by h(e) and t(e) (there is a choice to be made here if h(e) 7^ t(e), but the homology groups do not depend on these choices). Let ZE and Z be the free abelian groups on the sets E = E(G) and V — V(G). Define the group homomorphism d : ZE -> ZV by d(e) = h(e) —t(e) and extend linearly. The homology of a graph G is the homology of d : ZE -> Z y . That is, the first homology of G is Hi{G,Z) zeroth homology of G is HQ(G,Z) = Kerd, and the = Coker<9. The reader seeking more details of this construction may consult [Mas91]. L e m m a 4.1.2. If G is a connected graph, then the image of d : ZE -^ ZV is zv$ ={xezv Y, xv = ° veV where xv denotes the coefficient of x on the basis element v. Proof. The image of each edge e is h(e) — t(e), which is in Z • . By the connectedness assumption, HQ(G,Z) > Z. The kernel of the degree map is Z ' , which agrees with the image by exactness. • The homology of a graph is a direct sum of the homology of each connected component of the graph, so the lemma generalizes to disconnected graphs: Corollary 4.1.3. The image of d : ZE -> Zv zy'° = I x e z V y ^ veV(Ga) is xv = 0 for all connected components Ga of G 93 The first homology is a subgroup of a free abelian group, thus free abelian itself. The zeroth homology is a direct sum of copies of Z, one for each connected component of G. In particular, the homology has no torsion. To get a configuration, tensor with a field K: 0 - • Hi(G, Z) ® K -> KE ^ ^ Kz -> H0(G, Z) <g> AT -^ 0. By direct calculation or the universal coefficient theorem, Hi(G, K) = Hj(G, Z) <g)K. I write Hi(G) for i/j(G, K ) , and h{{G) for dim^- Hi(G). In some of the lemmas, I apply the Euler characteristic of this chain complex, which in this case gives X(G) = h0(G)-h1(G) = \V\-\E\. (4.1) The next step is to develop a dictionary to translate between the topological definitions and combinatorial definitions. First note the following notational convention: N o t a t i o n 4.1.4 (Removing the Interior of An Edge). If e is an edge in a graph G, then by definition, both vertices h(e) and t(e) are in e. In particular, e is a subgraph of G. The notation G — e will be used as a shorthand for removing the interior of e from G; the vertices remain. In particular, V(G — e) = V(G) because no vertices are removed, and thus the vertices of every edge is still in G — e, so G — e is still a subgraph. If G — e denoted removing all of e, it would fail to be a graph if h(e) or t(e) had multiple incident edges. The following terms are basic in graph theory, but I prefer the simplicity of the topological definitions. I prove below that the graph-theoretic definitions are equivalent. Definition 4.1.5 (Graph Terminology). A forest is a graph G with H\(G) = 0. A tree is a connected forest. A spanning forest of a graph G is a maximal subgraph F with respect to inclusion among all subgraphs that are forests. A spanning tree of a graph G is a connected spanning forest. The complement of a spanning forest is 94 called a chord set] many arguments could be given in terms of chord sets, but I have favored spanning forests instead. A cycle is an element of Hi(G, Z), or more generally H\{G). A cyclic graph is a graph with H\(G) ^ 0; a graph is acyclic if H\{G) — 0, i.e., it is a forest. A circuit is a connected cyclic graph with no cyclic subgraphs. In particular, for every edge e in a circuit C, h\(C — e) = 0. A two-forest is a spanning forest minus the interior of an edge. If each connected component of G has a single vertex, then G has no two-trees because the spanning forests have no edges. If the spanning forest is a spanning tree, then a two-forest is called a two-tree. The complement of a two-forest is a minimal cut set. A minimal cut set C will not contain the vertices of the complementary two-forest F, and thus there can be edges whose interior is in C but whose vertices are in F; in particular, C may not be a graph. Every subgraph Y C G induces an inclusion Hi(Y) C Hi(G). In particular, subgraphs of forests are forests. The subgraphs that are circuits and forests play an important role in identifying specific elements of the homology groups. They will also provide a combinatorial description of the first graph polynomial (Proposition 4.2.4). The two-forests and minimal cut sets are useful for describing the second graph polynomial (Proposition 4.2.12). For the combinatorial perspective, I present the following lemmas. Lemma 4.1.6 (Homology of Attaching 1-Cells). Suppose Y is a subgraph ofG, and e £ E{G) — E(Y) with both vertices of e in Y. Then either 1. hY(Y U e) = hi(T) + 1 and hQ(Y Ue) = h0{Y), or 2. hi{Y U e) = hi{Y) and hQ(Y U e) = h0{Y) - 1. In particular, if Y is connected, the second case cannot occur. 1. This definition differs from some but not all graph theory literature. The graph theory literature does not seem uniform about the definition of a cycle, circuit, etc., whereas the topology literature is a little more consistent in its definition of cycle. I have used the topological definitions as motivation here. 95 Proof. The Euler characteristic satisfies x(rue) = x ( r ) - i because there is one new edge and no new vertices. Regrouping the terms in this equation gives (Mr) - M r u e)) + (Mr u e) - MH) = 1. (4.2) Because e is connected to T, Mrue)<MH. Because F is a subgraph of T U e, M r ) < /ii(rue). Therefore, both terms on the left side of Equation (4.2) are nonnegative, and so exactly one term is equal to 1 and the other to 0. • L e m m a 4.1.7 (Forests Extend to Spanning Forests). Every forest F in a graph G is contained in a spanning forest of G. Proof. Consider the set T of forests that are contained in G. Every vertex of G is in J7, so T is nonempty (recall that I have assumed all graphs have V ^ 0). The set T is a subset of the finite set of all subgraphs of G partially ordered by inclusion. Therefore, T has maximal elements, which are spanning forests by definition. Corollary 4.1.8 (Existence of Spanning Forests). Every graph has a spanning • forest. L e m m a 4.1.9 (Spanning Forests Span Vertices). A spanning forest F of a graph G hasV(F) = V{G). Proof. The spanning forest is a subgraph so V(F) C V(G). Adding a 0-cell to a graph cannot change the first homology. Therefore, if v G V(G) — V(F), then the graph F U v is a forest properly containing F, which is a contradiction. • 96 L e m m a 4.1.10. Suppose F is a spanning forest ofG. For every edge e G E(G) — E(F), hi{F\Je) = \ and h0(F U e) = h0(F). Remark 4.1.11. By the maximality of the spanning forest F, the larger graph F U e is not a forest, so hi(FUe) > 1. The Euler characteristic shows that it must be an equality, but intuitively it is impossible to increase the first homology by more than one when a single 1-cell is added. Proof. By definition of spanning tree, ftl(FUe) > h1{F) = 0. The vertices of e are in F (Lemma 4.1.9), so applying Lemma 4.1.6 gives the conclusion. • L e m m a 4.1.12. A spanning forest of G is a spanning tree if and only if G is connected. Proof. Suppose T is a spanning tree. It is a spanning forest and contains all vertices of G by Lemma 4.1.9. Therefore, it connects all vertices of G, and every edge of G is connected to a vertex, so G is connected. Suppose G is connected and T is a spanning forest. By Lemma 4.1.9, V(T) V(G). = If T has multiple components, then some pair of them, T\ and T2, can be connected by an edge e in E(G) — E{T) because the vertices of all edges are in T. In particular, HQ(T U e) < ho(T). h\(T U e) = h\(T) By Lemma 4.1.6, IIQ(T U e) = IIQ{T) — 1 and = 0. Therefore, T U e is a forest, contradicting the maximality property of the spanning forest T. • L e m m a 4 . 1 . 1 3 . A tree contained in a connected graph G is a spanning tree if and only ifV(T) = V{G). 97 Remark 4.1.14. Note that this is not true for a disconnected graph G and a forest F in G. For example, Lemma 4.1.16 shows that two-forests have V(F) — V(G), too. Proof. Lemma 4.1.9 shows that a spanning tree must have V{T) versely, suppose T is a subtree with V(T) and h\(T) = V(G). = V(G). Con- Because it is a tree, IIQ(T) = 1 = 0. To show it is a spanning tree, I must show that h\(T U e) > 0 for every edge e € E(G) — E(T). The tree T contains the vertices of e by assumption and is connected, so h\(T U e) = 1 by Lemma 4.1.6. • L e m m a 4.1.15 (Spanning Forests are Componentwise Spanning Trees). A subgraph F of G is a spanning forest if and only if F n G ? is a spanning tree of Gj for each connected component Gj of G. In particular, ho(F) = /irj(G). Proof. If F is a spanning forest of G, then F D T is a spanning forest for every subgraph T because a larger forest contained in F would also be contained in G. In particular, for each connected component G\ of G, FflG,; is a spanning forest of Gj. By Lemma 4.1.12, F C\ Gj must be a spanning tree because G-t is connected. If F fl Gj is a spanning tree of Gj for all components, then F, which is the union of each of these spanning trees, is a forest. For every edge e in E(G) — E(F), e is in one of the components Gj, so 1 = hi{(F n Gj) U e) < hi(F U e) because F fl Gj is a maximal forest in Gj. Thus F satisfies the maximality condition of a spanning forest. • L e m m a 4.1.16 (Alternative Characterization of Two-Forests). A subgraph F of G is a two-forest if and only if 1. h0(F) = ho(G) + 1, 2. hi(F) =0, 3. V(F) = and V{G). 98 Proof. Suppose F is a two-forest. Let F be a spanning forest such that F — e = F. By definition, F — e is shorthand for removing the interior of e, and by Lemma 4.1.9, V(G) = V{F) = V{F - e) = V(F). As a subgraph of a forest, F is a forest, i.e., hi(F) < h\(F) = 0. All vertices are in F, so Lemma 4.1.6 implies h0(F) = ho(F) + 1 = /i 0 (G) + l. Now suppose F satisfies 1. h0(F) = h0(G) + 1, 2. hi{F) = 0, and 3. V(F) = V(G). In particular, F is forest containing all vertices of G. For each component G\ of G, F n G j is a forest containing all vertices of Gi, and in particular, ho(F n Gz) > 0. The decomposition H0(F) = Q)Ho(FnGl) i shows that in fact all h^{F D Gi) = 1 with one exception HQ(F n Gj) = 2. Then for z ^ j , F n Gj is a spanning tree of Gj by Lemma 4.1.13. The component Gj is connected, but F n Gj has two components and all vertices of Gj. Therefore, there is some edge e that connects these two components, and (F n Gj) U e must be a spanning tree. By Lemma 4.1.15, F U e is a spanning forest, and thus F is a two-forest. D Lemma 4.1.17 (Alternative Characterization of Minimal Cut Sets). A subset C of G is a minimal cut set if and only if it is a minimal subset of the set of interiors of edges of G with the properties 99 1. hi(G-C) = 0, and 2. h0{G-C) = h0(G) + l. In particular, C contains no vertices and is never a graph. Proof. This is just a restatement of Lemma 4.1.16 for the complement. Perhaps two points are worth noting. If C is a minimal cut set corresponding to a two-forest F = G — C, then C is a set of edge interiors because V(F) = V(G). The set C is minimal among such sets because a two-tree cannot properly contain another two-tree. • Recall that C — e means removing the interior of e so that when C is a graph, C — e is guaranteed to be a graph. L e m m a 4.1.18 (Alternative Characterization of Circuits). A graph C is a circuit if and only if 1. /ii(C) = l, 2. h\{C — e) = 0 for all edges e G E(C), 3. h0(C - e) = 1 for all edges e E and E(C). Proof. The definition of circuit is that h\(C) ^ 0, every proper subgraph is acyclic (hi(T) = 0 for T C\ C), and C is connected, so ho(C) = 1. If C is a circuit, then the proper subgraph C — e satisfies h\(C — e) = 0, so the only condition that must be verified is IIQ(C — e) = 1 (i.e., C — e is connected). Both vertices of e are in C — e, so Lemma 4.1.6 implies IIQ(C — e) = ho(C) — 1. Conversely, both vertices of e are in C — e, and thus h0(C) using h\(C) h\(C) = h0(C - e) = 1 = 1, h\(C — e) = 0, and Lemma 4.1.6. In particular, C is connected and 7^ 0. Every proper subgraph T of C is missing some edge e of C, so T C C — e, and thus hi(T) = 0. Therefore, C is a circuit. • 100 Figure 4.2: A circuit with 6 edges and vertices. L e m m a 4.1.19 (Combinatorial Description of Circuits). A graph C is a circuit if and only if there is an labeling of the vertices of C': vi,..-, vn such that E(C) is the set of exactly n edges {e\,..., en} where ez- connects vi to t'j + i for i < n and en connects vn to v\. Proof. Induct on the number of edges in C. A graph with no edges is automatically acyclic. There are two connected graphs with one edge e depending on whether the vertices are distinct. If h(e) ^ t(e), then the graph is contractible, and thus acyclic. If h(e) = t(e), then the graph is cyclic, the set of vertices is trivially ordered, and there is one edge connecting v\ to itself. Suppose graphs with fewer than n edges are circuits if and only if the vertices and edges satisfy the description in the statement of the lemma. Let C be a graph with n + 1 edges. If C satisfies the condition on vertices and edges, then C satisfies the conditions of Lemma 4.1.18. 2. In the graph theory, such a graph is often called an n-cycle. terminology differs because a topological n-cycle is different. As I have noted, my 101 Conversely, suppose C is circuit, and let e be an edge of C, and in particular a subgraph. If e has a single vertex, then H\(e) 7^ 0, and thus e = C by the definition of circuit; this contradicts the fact that n + 1 > 1. Therefore, e has two distinct vertices. Therefore, C is nomotopic to C/e, and thus Hi(C)^Hi(C/e). Similarly, C — f is homotopic to (C — / ) / e = (C/e) — / for every edge / ^ e, so for all i, Hi(C-f)<*Hi((C/e)-f). The topological space C / e is still a graph; it has n edges and one vertex fewer (the vertices of e get identitified). The isomorphisms of homology imply that the conditions of Lemma 4.1.18 are satisfied for C/e, and thus C/e is a circuit. By the induction hypothesis, C/e satisfies the conclusion, so in particular the vertices can be ordered so that v\ is the vertex to which e was contracted. In C, one vertex of e is adjacent to V2 and the other to vn; label these v\ and vn+i, Remark respectively. • 4.1.20. Lemma 4.1.19 makes a key connection between the combinatorial and the topological perspective; it is unnecessary for the topological arguments that I make. However, the combinatorial perspective is essential. For example, Theorem 4.4.8 is a famous theorem from graph theory for which I use this equivalent combinatorial definition of circuit. L e m m a 4.1.21 (Cyclic Graphs Contain Circuits). A graph G has h\(G) ^ 0 if and only if G contains a circuit. no Conversely, a graph is a forest if and only if it contains circuits. Proof. If G contains a circuit C, then the inclusion H\{C) C H\(G) proves h\(G) is nonzero. Suppose hi(G) ^ 0. If Gi,...,G n are the components of G, then the isomor- phism #l(G9^#i(Gi) ©•••©#!(£„) 102 Figure 4.3: A circuit with three edges. a a a ^ e2 Figure 4.4: Spanning trees of the circuit with three edges. shows that there is at least one connected component of G with h\(Gi) ^ 0. Consider the set C of all connected subgraphs of G with h\(G) =^ 0, which is nonempty because it at least contains G{. The set C is finite because the set of subgraphs is finite, so every minimal element of C is a circuit by definition. Example • 4.1.22 (Circuit with Three Edges). Consider the graph G of Figure 4.3 where I have used arrows to indicate an orientation on the edges that I will use for computing homology. The homology H\(G) is generated by the circuit £ = e\ + 62 + e%. The spanning trees of G are shown in Figure 4.4, its two-trees in Figure 4.5, and its minimal cut sets in Figure 4.6. In this case, the minimal cut sets of G are the spanning trees without the vertices, but this is not true in general. Definition 4.1.23 (Circuits as Integral Homology Classes). Every circuit C has Hi(C,Z) = Z. I will ambiguously use C to denote the circuit C or either of the generators of Hi(C,Z). The context should determine whether I mean the graph or an integral 103 e2 / Figure 4.5: Two-trees of the circuit with three edges. Figure 4.6: Minimal cut sets of the circuit with three edges. homology class. When a statement about the integral homology class depends on the choice of generator, I will clarify how the statement depends on the choice. Recall that Xe : K —> K are the linear forms on K They restrict to the subspace defined by the dual basis. H\(G). D e f i n i t i o n / L e m m a 4.1.24 (Circuit Basis). Let G be a graph. 1. For every circuit £\ contained in G, there are circuits £2, • • •, £\u (G) that < £1,..., %n G su °h Ifr IQ\ > is an integral basis for H\(G, Z). / call such a basis is a circuit basis. 2. Every circuit £\ can be completed to a circuit basis \£\, • • • Ah-ilG) f suc 1 ^ ^ia^ there are edges ex, . . . , e^ /Q\ with e^ E £j if and only if i = j . In this case, the set of edges £-{ e l>-"> e fti(G)} forms a spanning forest F, and I call the basis < £\,. . . , £L ,Q\ > a circuit basis with respect to the spanning forest F. 3. With circuits and edges as in the previous part, the quadratic X? : Hi(G) -+ K forms 104 for i = 1 , . . . , hi(G) are given by xf{b1ii + '-- + bhl{G)ehl{G)) = bf. This does not make any claims about Xf for i > h\(G). Proof. First note that if h\{G) > 1, then G must have a circuit l\ by Lemma 4.1.21. Removing the interior of an edge e\ from the circuit £\ leaves a tree i\ — e\. The remaining tree can be completed to a spanning forest F (Lemma 4.1.7). The edge e\ is not in the spanning forest because 1 = M ^ l ) = M ( 4 " ei) U e i ) < hx(F U ei). The Euler characteristic determines that there must be h\{G) edges not in the spanning forest, so label those that are not e\ as e 2 , . . . , %-.IQ\- For each edge ej, h\{e{ UF) = 1 (Lemma 4.1.10), so there is a circuit £{ that generates H\{ei UF). Note that e^ £ £j because / i l ( ^ - e i ) < / i i ( F ) = 0. Moreover, e^ £ £j if and only if i = j because e^ ^ F U ej for z =£ j . In particular, these circuits satisfy Xi(tj) = ±^j> which proves the last part of the lemma. If E j = i G ) fy^' = 0, then for each i = 1,.. ., hi(G) ±b; = 0, so these circuits are linearly independent. They must span because there are h\(G) of them. • 105 Figure 4.7: A simple graph to demonstrate the first graph polynomial. 4.2 Configurations from Graphs The first homology group of a graph G is a configuration in K . Therefore, the following definition is possible: Definition 4.2.1 (First Graph Polynomial). Suppose G is a graph with h\(G) > 0. The first graph hypersurface uration Hi{G) C K mial for H\(G) C K is the configuration hypersurface defined by the config- . The first graph polynomial ^Q(A) is a configuration polyno- computed in a basis for i 7 i ( G , Z ) . This polynomial is also called the first Kirchhoff polynomial or the first Kirchhoff-Symanzik polynomial in the literature after Kirchhoff who used it to study electrical networks and Symanzik who applied it to quantum field theory. Example 4.2.2. Consider the graph in Figure 4.7 where I have used arrows to indicate an orientation of the edges that I will use to compute H\{G). e e The circuits l\ = e\ and £2 = 2 ^ 3 form a basis for H\(G, Z). Let L\ and L2 be the dual basis elements 106 of H\(G)y. The linear forms from the edges can be written in this basis as eil#i(G) : = Li e2l#i(G) : = L2 e^HiiG) := -L2 Therefore, the restricted quadratic forms are X "= Ll X = e1\Hi(G) e2\H1(G) X e3\H1(G) L 2 =L2- Thus, the first graph polynomial is 3 *G(A) = detY,AiXl.\Hl{G) i=l = det (AiLi + A2L2 det l ' * ° = AiA2 0 A2 + A3y + A1A3. + A3L2 The term ^ 1 ^ 2 corresponds to the spanning tree e3, and the term A\A3 corresponds to the spanning tree e2. I verify that this correspondence is general in Lemma 4.2.3. By Lemma 4.1.24, the circuit bases provide a nice combinatorial object to use for the basis of H\(G) when computing the first graph polynomial. However. I must first prove that the first graph polynomial is well-defined. L e m m a 4.2.3 (First Graph Polynomial is Well-Defined). The first graph polynomial is well-defined, polynomial ^JJ SQ\ . i.e., all integral bases of Hi(G,Z) yield the same configuration Proof. The coefficient of YiceC ^o in ^G(A) is Plucker,^./^ (G)) 2 computed in an integral basis for H\(G, Z), where | C | = h\(G). The projection H1(G)C • KE KC is induced by tensoring the projection of free abelian groups Hi(G,Z) c »ZE if with X. Consider the commutative diagram of free abelian groups with short exact 108 sequences in the vertical and horizontal directions: 0 jE-C 0 - Hi(G,Z) • ZE d ZVfi »0 if 0. The map ft is surjective if and only if a is injective. The map a is injective if and only if the subgraph with edges E — C has h\(E — C) = 0 because a is induced by d. In particular, ft is surjective if and only if E — C is a forest. The map ft is injective if and only if a is surjective, which happens when the edges E — C are connected and spanning in each component of G. Therefore, if H\(G) —> Kc is an isomorphism, then ft : H\{G,Z) —> lF is an isomorphism of Z-modules and E—C is a spanning forest. In particular, det(/3) = ± 1 , so P l i i c k e r a ( i f i ( G ) ) 2 = 1. • The proof shows more: P r o p o s i t i o n 4.2.4 (First Graph Polynomial, Combinatorial Formula). Let G be a 109 graph with h\{G) > 0. The first graph polynomial is chord sets e£C CcE = n-4. £ spanning forests FcE e^F The argument for Lemma 4.2.3 is taken from [BEK06]. I have included it here because it provides the foundation for a similar treatment of the second graph polynomial. I note the well-known combinatorial corollary: Corollary 4.2.5. For a graph with h\(G) > 0, ^G(^)UP=1 = ^ e number of spanning forests ofG. The first graph polynomial appears in the denominator of the following parametric Feynman integrals in quantum field theory: ^G(PA) (43) 'U-h^AT The chain of integration a is all positive real values of the A variables. The integral is a period of the complement of the first graph hypersurface relative to where that complement meets the boundary of the chain of integration, namely the coordinate hyperplanes. Note that the coordinate hyperplanes are the trivial configuration hypersurface corresponding to the configuration K C K . The quantum field theory behind this integral can be found in Section 6-2-3 of [IZ80]; the condensed presentation of [BEK06] is sufficient and focused on the applications I have in mind. The expression $Q(P, A) is the second graph polynomial. This second polynomial receives less attention in the literature because the integral is not a period of the complement of the second graph hypersurface. Therefore, the literature often refers to the graph hypersurface or graph polynomial without reference to the first or no second; in such cases, the first one is meant. I now show that the second graph polynomial is also a configuration polynomial. Actually, I will define it to be a configuration polynomial, and then Proposition 4.2.12 will show that the configuration polynomial has the same formula as the second graph polynomial defined elsewhere in the literature (for example [BK08], [Nak71], or [IZ80]). E d(K ) In fact, it is a family of configuration polynomials parametrized by v C K. d{KE) By Corollary 4.1.3, =ZV>°®K = {PeKV | J2 Vv = 0 for all components C of G}; veV(C) I denote this space by K ' . Remark 4.2.6. The set K ' u is often called the space of momenta or external mo- menta for the graph G. This terminology is motivated from physics where external edges of a graph carry nonzero momenta to a vertex in the graph subject to conservation of momentum. More specifically, the idea is that an element of K labels the momenta along the edges of the graph (if q = YleeE Qee' then there is momenta qe along edge e). Then the coordinate pv of dq = p at a vertex v indicates the net flow of momentum into the vertex v (or out of v depending on sign and convention). The condition YlveV(C) Pv = ® for connected components of G corresponds to the conservation of momentum for each component of the system. The vertices v for which pv is nonzero indicate where momentum is flowing into or out of the graph. Fix a nonzero p € Kv,{i, H\{G) + Kq. and suppose dq = p. Let H\(G,p) = d~ (Kp) = This is the relative homology group for Kp considered as a chain complex concentrated in degree zero included in the chain complex for the graph G. Ill More specifically, the kernel and homology of that inclusion of chain complexes give Hi(p) = 0 0 • Kp • H0(p) = Kp n d K1 Hi(G) •* K V H0(G) n K Hi(G,p) E »KV/Kp ~H0{G,p). There is an induced long exact sequence from the snake lemma: 0 -+ HX{G) -+ Hx{G,p) -* H0(p) ^ H0(G) ^ H0(G,p) -+ 0. Because p G K V,0 H0(G) = Kv/Kv>° and the map H\(G,p) = KV/(KV>° + Kp) = HQ(G,p), —> HQ(P) = Kp is surjective. Definition 4.2.7 (Second Graph Polynomial). Let p e K^^>^ second graph hypersurface tion H\{G,p) C K YQP is the configuration hypersurface for the configura- . The second graph polynomial <&Q(P,A) polynomial ^JJ. <Q p\ (A) with respect to a basis of H\(G,p) basis for Hi(G,X) and an element q E K is the configuration consisting of an integral such that dq = p. In the literature, this polynomial is also known as the second Kirchhoff Kirchhoff-Symanzik be nonzero. The polynomial or the second polynomial. The second graph polynomial may be defined when h\(G) — 0 as long as there are nonzero p in Kv,°. There are nonzero p G K - as long as G has a component with more than one vertex. Only a graph composed of disconnected vertices and no edges fails to define either graph polynomial. 112 Note that every circuit basis for H\ (G) extends to a basis for H\ (G, p) by selecting any q G H\{G,p) such that dq = p. I will derive the standard combinatorial formula for the second graph polynomial below. First, I present an example. Example 4.2.8 (Circuit with Three Edges). Consider the graph of Example 4.1.22. Let p = YlveV PvV ^ e an element of K ' . Suppose that q 6 K is a lift of p: p = dq 3 = d^2qiet i=l = («3 - Ql)a + (qi ~ q2)b + {q2 ~ 93) c = paa + pbb + pcc. Then £ = ei + e2 + e% and q are a basis for Hi(G,p) as in Definition 4.2.7. Let L and Q denote the dual basis of Hi(G,p)^. all i, the linear forms Xei\jj Because Xei(£) = 1 and Xe-(q) = qi for IQ ^ are X ei\Hl(G,P) = L + Q'i.Q Therefore, the bilinear forms X^.\^j IQ ,p\ are Xl\Hl(G,P) = L2 + 2qlLQ + qJQ' 113 Calculating the second graph polynomial in this basis gives *G,p(A) = det J2 ML2 + 2qeLQ + q2Q2) eeE ( = Ai+A2+A3 \qiAi 91^1+92^2+93^ + q2A2 + 93^3 q\Ai + q\A2 + 93^3/ = (Ax + A2 + A2,){qlAi + q2A2 + 93^3) - (91^1 + 92^2 + 93^s) 2 = E «?A? + E QiAiA3 ~ E 9?A? " E mjAAj i^j i i^j i J2qzAiAJ~^WAtAJ = J2^+g]~2q^A^AJ = i<j = E ^ " Qj)2AiA3 i<j = PIAXAS + p2hAlA2 + P2CA2A3. In particular, the AjAj correspond to the minimal cut sets of the graph, and their coefficients (qi — qj) correspond to the square of the value of p on one of the components of the corresponding two-tree. I derive this general formula in Proposition 4.2.12. Lemma 4.2.9 (Second Graph Polynomial is Well-Defined). The second graph polynomial is well-defined. That is, Pliicker 2 c(Hi{G,p)) is independent of the choice of an integral basis for H\(G, If) and a preimage q of p for all subsets C of E with \C\ = h\(G,p) = h\(G) + 1. 114 Proof. I use a diagram similar to the one in the proof of Lemma 4.2.3: 0 0 KE • Hi{G,p) • Kv-Q/Kp • 0 KC 0. Again, a is injective if and only if (3 is surjective, and a is surjective if and only if 3 is injective. The maps factor for every edge e G C: a :KE~C Pe -H^G) ~> AT(S-C)ue ^ ^ H!(G,p) ^ RVfi KC - _^ KVfi/Rp KC~e As in the proof of Lemma 4.2.3, pe is an isomorphism if and only if (E — C)Ue is a spanning forest. If Plucker^(i/x (G, p)) is zero, then p is not an isomorphism, and there is nothing to check. If PliickerQ(Hi (G,p)) from H\{G) isomorphism. to K is nonzero, then 8 is an isomorphism, and the map is an inclusion. Then there must be some e such that 6e is an For this e, (E — C) U e is a spanning tree, and thus (E — C) is a two-forest. In particular, it suffices to compute Plucker ( ^(i : fi (G,p)) for minimal cut sets C, though there may be minimal cut sets for which Pliicker(j(Hi(G,p))2 is zero 115 depending on p. If C is a minimal cut set, then there is an edge e such that (E — C) U e is a spanning forest, and hence j3e is an isomorphism. Whether 8 is an isomorphism depends on the particular value of p and the minimal cut set C. If G is connected, then G — C is a pair of trees, but if G is disconnected, then G — C has more than two components. In either case, there is a unique connected component T of G for which r n (G — C) is has two components T\ and T<i- Let F]_ U F'2 be a partition of the two-forest G — C such that Tj C Fj. For these forests, define veF, With the exception of Tj, the components of Ft cover the vertices of a component of G, and summing pv over those vertices is zero by definition of K • . Therefore, it suffices to sum over the vertices in Tf m Ft(p) =^PvveTt Note that mFl (p) = ~m,F2 (p) because J2veTPv 0 for the connected component T that is disconnected by the minimal cut set C Define the momentum sc(p) of p on the cut set C to be = mF.(p)2 for both forests JF\ and F2. The next two lemmas complete the proof by showing that PlnCkeTC(H1(G,p))2 = sc(p), which is basis independent. In particular, 3 is an isomorphism if and only if is nonzero. sc(p) • 116 First, I need to put S(j(p) in a form that relates it to a lift q G K^E with dq = p L e m m a 4.2.10 (Vertex Momenta in terms of Edge Momenta). Suppose C is a minimal cut set of a graph G partitioning element of Kv>®, and let q G K G — C into F\ and F2. Let p be a nonzero map to p: dq = p. rn Qe Fl (P) = Y qe ~ Y eeC h(e)eFt for both i = 1,2. In Then eeC t(e)<=Fi particular, ( £ *- £ sc(p) . eeC \h(e)eFl eeC t(e)£Fi Qe , / Proof. The proof is the same for F\ and F2; I prove it for F\. v G Kv has a dual element Xv G (Kv)y. The basis element Denote the restriction of Xv to Kv>° by Xv, as well. By definition of q and nip (p), m Pv F1 (p) = Y veV(Fx) = Y XM VGV(FI) = Y X viPq) veV(Fx) = Y Xv [ Y veV{Fx) = Y qeh ^ ~qet^ \eeE{G) Y veV(F!)eeE(G) QeXv(h(e) - t(e)) 117 The edges in F2 have vertices in ^ ( i ^ ) , which is disjoint from V(Fi), so m F1(p) = E E 1eXv(h(e)-t(e)). (4.4) veV(Fi)eeE(G-F2) Note that 1 if v = h(e), Xv(h(e) - t(e)) = { -l ifv = t(e), 0 otherwise. If both h(e) and t(e) are in V(Fi), then qe appears in the sum (4.4) with both a plus and a minus, so it cancels out. For example, every edge e 6 F j has both h(e) and t(e) in V(Fi), so m F1(p) = E E qeXv(h(e) - t(e)) veV{F1)eeE(G-F1-F2) = E ^2 QeXv(h(e) - t(e)) veV(Fi)eeC =E E &~ E ^ eeC \/i(e)ey(Fi) i(e)e7(Fi) which completes the proof. • Lemma 4.2.11 (Pliicker Coordinates are Momenta). Suppose C is a minimal cut set of a graph G partitioning G — C into F\ and F2. Let p be a nonzero element of KV,Q. Let (3 be the projection from H\{G,p) to K . Then in a basis of H\(G,p) consisting of q in the preimage of p and an integral basis of H~i(G,Z), det(/?) = ±mFl(p) In particular, Pluckevc(Hi(G,p)) = S(j(p). = TrriF2(p). 118 Proof. Let m = hi(G). Write (3 in a basis as in the statement of the lemma: / X^q) Xx(£2) ... *l(Wl) \ X2(q) X2(£2) ... X2(£m+1) \xm+i(q) xm+i(i2) ... xm+i(£m+i)J The columns correspond to a g such that dq = p followed by the integral basis ^2) • • • > ^m+1 °f H\{G, Z). The rows correspond to the elements of the cut set C = {ei,...,em+i}. Write qi for Xi(q). Expand the determinant of (3 along the first column of this matrix: det/5 = qi Plucker c -_ e i (//i(G))-g 2 Plucker c „ e 2 (Fi(G'))+ .••±9mPluckfirc_em+1(^i(G)). The Plucker coordinate Pliicker(7_e.(ifi(G')) is computed in an integral basis, hence as in Lemma 4.2.3, it is 0 if (E — C) U e^ is not a spanning forest and is ±1 if (E — C) U ei is a spanning forest. Comparing the formula for det/3 with Lemma 4.2.10, I need to check that 1. the signs on qi and qj in det f3 agree if the heads of both or tails of both are in Fy, 2. and the signs on qi and qj are opposite if the head of one and the tail of the other is in Fy 3. but the coefficient ( - l ) i + 1 Plucker c _ e .(#i(G)) of q{ in det 3 is 0 if (E-C)Uet is not a spanning forest, which happens exactly when both the head and the tail of ej are in F\ or both are in F2 (in agreement with Lemma 4.2.10 where qe appears twice in the sum but with opposite signs). 119 To complete the proof, I compare the signs of q\ and 92 assuming ei and e^ fit into one of the first two cases above. In particular, (E — C) U ei is a spanning forest. At this point, the value of det (3 only depends on the integral basis of Hi(G,Z) for an overall sign, so I am free to choose the basis {£2, • • •, £m+l} to be a circuit basis for Hi(G,Z) with respect to the spanning forest (E-C)Uei as in Lemma 4.1.24. That is, &i G £{ for i = 2,. . . , m -f- 1 ej £ t,- for j ^ i. Note that i 7^ 1 in these formulas as e\ may well be in all £j. Because I have assumed that e\ and e<i do not fit into the third case above, both (E — C) U ei and (E — C) U e2 are spanning forests. Therefore, the circuit generator £2 of ifi ( ( F — C) U e\ U e2) must contain both ei and e2 because M^2-ei)<M(£-C)Ue2) = 0 ^ l ( ^ 2 - e 2 ) < / i i ( ( ^ - C , ) U e i ) = 0. Therefore, -^1(^2) — -^2(^2) — i l if -^1(^2) = on ly o n e OI e l or e 2 nas its head in F\, and ± ^ 2 ( ^ 2 ) = ± 1 if both ei and e2 have their heads in the same Fj. By changing the orientation of every edge in £2 if necessary, I may assume ^2(^2) = 1? so that the cases are Xi (^2) = 1 or X i ^ ) = - 1 . Note that the determinant begins det 8 = q\ det 71 — q2 det 72 + • • • 120 where ( X2(£2) x2(£3) x3{£3) x3(h) 71 \Xm+\{£2) x2{£m+i) x3(£m+\ Xm+i(ellm+i) Xm+i(£3) (x2{£2) 0 \ X2(h) X3{£3) X2(£m+i) •• .. (h) ••• J \ X^m+l) (ellm+i)J Xm+i and ( Xx{£2) XY{iz) x3{£2) x3(e3) X\{tm+l) 72 \ X3{£m+\ \Xm+i(£2) Xm+i(£3) (xx{h) xx{£3) . 0 X3(£3) . 0 Xm+1(£3) \ ... Xm+i(ellm+i) Xl^nUrl) J \ X3(£m+i) Xm+i(ellm+i) J With the exception of the first row, 71 and 72 agree, so by the multilinearity of the determinant, det(7i) ± det(72) ({x2±xl){£2) det 0 0 (x2±x1)(e3) X3(£3) Xm+i{h) (X2±X1)(£m+1)\ XMm+l) Xm+i(ellm+i) J 121 Now I consider the two cases noted above: ( X 2 - X 1 ) ( £ 2 ) = 0ifX1(£2) = l (X 2 + X i ) ( ^ 2 ) = 0 i f Xi(£ 2 ) = - 1 In both cases, the first column of the preceding matrix is zero. Therefore, det 71 det72 — det 72 if only one of ei or e 2 has its head in F i , if both ej have their heads in the same Fj, and det/3 (det 7i)(q i i — <?2) + • • • if only one of e\ or e 2 has its head in F i , (det7i)(gi + 92) + • • • if both ej have their heads in the same F,. This completes the comparsion of the signs on q\ and g2 and hence the proof. D P r o p o s i t i o n 4.2.12 (Second Graph Polynomial, Combinatorial Formula). The second graph polynomial corresponding to a graph G and a nonzero momentum p £ K ' is $G(p,A) = min. = S C(P) I I Af J2 cut sets CCS X] 2-forests TCE f 6C S G _ T CP) I I A f f<£T Proposition 4.2.12 is false if p is allowed to be zero: d - 1 ( 0 ) is F i ( G ) , and the configuration polynomial of this configuration would be the first graph polynomial, but then the formula of this proposition would not hold for all p because SQ(0) = 0 for all C whereas the first graph polynomial is nonzero. Similarly, the sum in the first graph polynomial is over spanning forests, not two-forests. Corollary 4 . 2 . 1 3 . Let p = v\ — V2 for any two vertices v\ and i>2 in a connected 122 graph G. For each minimal cut set C, sG(p) is 0 if both vertices are in the same component of G — C and is 1 if the vertices are in different components of G — C. As a result, ^G P(A)\AF=I — the number of two-trees separating v\ andv2- The first and second graph hypersurfaces are configuration hyper surfaces in F(KE) defined by a quadratic form on the trivial vector bundles P(K E and P(K ) x Hi(G,p) ) x H\(G) respectively with values in the line bundle C L / ^ g J l ) . I write the degeneracy loci of rank at most k forms as Dk(H\{G)) and Dk(Hi(G,p)). Theorem 3.3.6 restated for these configuration hypersurfaces says Corollary 4.2.14 (Singularities of Graph Hypersurfaces). Let G be a graph, and let p be a nonzero element of K'°. Suppose h\(G) is nonzero and denote it by m. Then Sing> fc X G = £> m _fc_i(#i(G9) and Smg>kYG^p = Dm_k(Hi(G,p)). In particular, SmgX G = Dm_2{Hi{G)) For all nonzero p E KV', and S i n g l y = Dm_i(#i(G,p)). H\(G) is a hyperplane subconfiguration of Hi(G,p), so Theorem 3.4.3 in this context says Corollary 4.2.15 (Intersection of First and Second Graph Hypersurface). Let G be a graph, and p a nonzero element of K^ ,0 . For all k, (Sing> fc+1 XG) U (Sing> fc+1 YG^p) C (Sing>fe XG) n (Sing>fc YGp). In particular, (Sing x G ) u (Sin g r G)P ) c xG n r GiP . 123 4.3 Polynomial Formulas for Some Graph Operations The definition of the graph polynomials in terms of homology makes it possible to analyze how some topological operations on a graph are reflected by the polynomials. Propositions 4.2.4 and 4.2.12 express the graph polynomials combinatorially, so some more combinatorial operations can be analyzed too. One fun aspect of these polynomials is that many approaches (e.g., topological, combinatorial, algebraic) may prove the same result. I present multiple proofs for some of the results in this section to show the range of techniques. One theme that emerges is that the combinatorics of two-trees can get quite messy, and often the topological approach produces simple proofs using linear algebra. Most of these results are well-known, but Proposition 4.3.19 is particularly interesting and still needs an analysis of the graph motive as discussed below. L e m m a 4.3.1 (Restricted Graph Configurations). For every subgraph Y of a graph G, H1(G)nKE^ In other words, Hi(T) is the restriction KE(G), ForpeKV(T)fi) H1(G,p)nKE^ = to K = Hi(T). ' ' of the configuration H\(G) C H1(T,p). Proof. By definition, <9p is the restriction of 8Q to the subspace spanned by the edges of T, so the following diagram commutes: 0 • H^T) n • KE^ H - t Kv™ 0 • H^G) KEW - ^ #T(G),0 Because every element of H\(G) fl KE^> r goes to 0 in KV(G>'°, 0 . 0. it must already be 124 0 in the subspace KV^',Q. The same reasoning applies to the other equation using the diagram ffl(r!p)_^)i#(r),o/i{p 0 - HI(G,P) — KE^ -M 0 Kv^°/Kp D L e m m a 4.3.2 (Conditions for an Edge to Define the Zero Functional). Let e be an edge in graph G. The corresponding linear functional XP:KE^K restricts to both H\(G) 1. X \H-\(G) 2. Xe\j],(Gp) and Hi(G,p). Then = 0 i/ and only if e does not belong to a circuit, and = 0 tf an d on ty tf e does not belong to a circuit and there is a lift q of p such that Xe(q) =qe=0- Contrapositively, 1. Xe\fl <Q\ 7^ 0 if and only if e belongs to a circuit, and 2. Xe\p[ tQp\ 7^ 0 if and only if e belongs to a circuit or for every lift q of p Xe(q) = Qe ^ 0. Proof. If e belongs to a circuit c, then Xe(c) ^ 0, and c <G H\{G) C H\{G,p). versely, if e does not belong to a circuit, then H\{G — e) = H\(G), 0. and Xe\jj Con- (G-e) = 125 If Xe\H /Qp\ = 0, then Xe\H /G\ = 0, so e does not belong to a circuit. By assumption, Xe(q) = 0 for all lifts of p, and in particular there is one. Conversely, suppose e is in no circuit and there is such a q. Because e is in no circuit, Xe\jj (Q\ = 0, so the value of Xe(q) is independent of the lift q. In particular, if there is one q for which Xe(q) = 0, then Xe\H I Q „ \ = 0. • For an edge e G G, I use G — e to denote removing the interior of e: its vertices remain. L e m m a 4.3.3 (Removing a Trivial Edge Disconnects). An edge e in a graph G is not contained in a circuit if and only if HQ(G — e) > ho(G). Conversely, an edge e in a graph G is contained in a circuit if and only if HQ(G — e) = ho(G). Proof. If e is not contained in a circuit, then Xe\jj Hi(G) ^KE~e, /Q\ = 0 (Lemma 4.3.2), so and thus H1(G) = H1(G)nKE-e, which is H\(G — e) by Lemma 4.3.1. Apply Lemma 4.1.6 using G — e as the subgraph T. Of the two possible conclusions of Lemma 4.1.6, the first one is ruled out because h\(G) = h\(G — e). The second conclusion includes KQ{G — e) = IIQ(G) + 1. If e is contained in a circuit, then Xe\H KE~e, rG\ ^ 0 (Lemma 4.3.2), so H\(G) ^ and ffl(G-e)£#i(G) by Lemma 4.3.1. Apply Lemma 4.1.6 using G — e as the subgraph I \ Of the two possible conclusions of Lemma 4.1.6, the second one is ruled out because h\(G) > h\{G — e). The first conclusion includes HQ(G — e) = HQ{G). 3. In the physics literature, a graph G whose edges are all contained in circuits are called one particle irreducible, or 1PI. Therefore, if e is not contained in a circuit, then G is not 1PI. • 126 Proposition 4.3.4 (Removing a Nontrivial Edge). Suppose e is an edge in G contained in a circuit. If hi (G — e) > 0, then dAe*G(A) = yG^e(A)- If p is a nonzero element of K '- '' , Proof. Note that h\(G — e) = h\(G) (Lemma 4.3.3) and all vertices of G — e are in G, so KV(G),0 = KV{G-e),0 and there is no restriction on p in Lemma 4.3.1. The edge e is in a circuit, so Xe\JJ,(G) K ¥" 0 (Lemma 4.3.2). Therefore, H\(G) <j£ , and the formulas follow from Lemma 3.2.8 for restricting configurations be- cause Hi(G-e) = Hi{G)r\KE-e and HX(G - e,p) = Hi(G,p) n KE~e by Lemma 4.3.1. Alternatively, one may argue that e is not in a spanning forest T of G if and only if T is a spanning forest of G — e, which establishes the formula for the first graph polynomial. For the second graph polynomial, it suffices to consider the connected component of G containing e, and thus I assume G is connected. The edge e is not in a two-tree F of G if and only if one of the following conditions is satisfied: 1. There is an edge e / e such that F U e is a spanning tree of G, or 2. F U e is a spanning tree of G but F U e is not a spanning tree of G for all e ^ e. In the first case, F is a two-tree i n G - e coming from the spanning tree F U e . I claim that the second case cannot occur because e is in a circuit c. The second case says that there is no edge e for which F U e is connected. For every edge e not 127 in the spanning tree F U e, the vertices of e must be in the same component of the two-tree F because F U e is not connected. By Lemma 4.1.6, /i 0 (FUe) = h0(F) = 2 and hi(FUe) = l. Therefore, there is a circuit in F U e containing e and entirely contained in one component of F. There is one such circuit for each of the h\(G) edges not in the spanning tree F U e, these circuits are independent, and thus they span H\(G){ci., the proof of Lemma 4.1.24). The edge e is in none of these circuits, so Xe\jj• ?Q\ is identically zero contrary to e being contained in some circuit (Lemma 4.3.2). • Suppose T is a subgraph of G. Let G//F denote the topological space that is the quotient by each connected component of F. In other words, each component of T is contracted to a vertex, and the topological space G//T is still a graph. If T is connected, G//F is just G/T. Denote the components of F by F?-, and let V{ be the vertex to which Fj is contracted in G//T. T, V ^ :K Define the surjective linear map -• KV{-G/I^ by (v xvtv(r) n{v) = < [vi and extend linearly. For p e live Ti Kv^>, 2_^ pv = Tl(p)l For each component C of G, there is a component of G//T that I denote C / / F . For 128 e^(G), pv= Y veV(C) Y veV{C)-V{T) = veV(rnC) Pv+ Y pv J2 PV+ Y ^k v£V{c//F) r?:cc n v ^- Y veV(C//T) Therefore, TT restricts to a surjective linear map, also denoted 7r, n The kernel of n is Kv^fi y G . Kv(G),o ^ Kv(G//r),o^ for both Kv^ and KV(G^° because p e Kv^ goes to r 0 in i r ( / / ) when both 1. Vv = 0 for all v £ V{Y) (i.e., p e KV{T)), 2- TT(P)^ = YlveT-Pv = 0 IOT eacri and component T?; (i.e., p G In particular, the following sequence is exact 0 _> KV^'° -> KV^fi ^ KV(G//T),0 so for each nonzero p e A'' / (^ r )' 0 i exactly one of ^r,p or _> Q) ^Gl IT n(p) 1S defined. Lemma 4.3.5 (Projected Graph Configurations). Suppose T is a subgraph of G. The projection of the configuration H\(G) to K ^ ' v p is an element of K ^>-®. Hi(G//T). Ifp i Kv{?)$, Proof. Note that E(G//T) v If p G K ^>^, ^ > is H\(G//T). Suppose then the projection of H\(G,p) then the projection of #i(G,p) is H^Gf/T, is n(p)). = E{G) - E(F), and let vt be the vertex to which the 129 connected component rz- is contracted. I claim that the following diagram commutes: d KE(G) G KV(G) t TX ^E(G-T) d, G//T U KE(G//T) KV(G//T)t The vertices of each edge in F are in a single component Tj of F. Therefore, for a general edge e of G, n(dG(e)) = ir(h(e) - t(e)) h{e)~t{e) tih(e),t(e)<£V{T), Vi-t(e) Xh(e)eV(ri),t(e)tV(r), < h(e) - Vi if h(e) £ V{T), t{e) G V(Ti), and 0 ifee£(F). Following the other direction around the diagram, d G//r(^E(G^r)(e)) fdG//T(e) iie£E(r) \dG//T(0) iieeE(T) h{e)-t{e) = iih{e),t{e)<£V(r), Vi-t(e) Hh(e) = vieV(ri),t(e)iV(T), h(e) - v, if h(e) i V(T),t(e) < 0 ifeeE(r), e V(Ti), and 130 so the diagram commutes. Therefore, the following diagram commutes: 0 • * KE^G) ffx(G) — ^ _ d H\ UGt #i(G//r) — K E(G//T) K ^ ° •0 KV(G//T),Q I must show that the map MG ffi(G) "r)>ffi(G//r) is surjective; denote the image by VK. Combinatorially, this amounts to looking at the edges of a circuit in G//T, looking at them in G, and then filling in with a tree in T to get a circuit in G, but making this precise can be messy. Instead, look at the long exact sequence from the snake lemma: 0 -» # i ( r ) -+ KE(V ^ KV(V>° -> Hi{G//T)/W -+ 0. The last map is zero because the first three nonzero terms are the short exact sequence for the subgraph T. By exactness, jfi(G//r)/w = o, that is, Hi(G) surjects onto W = Hi(G//T). The same argument works for the second graph polynomial using one of the following commuting diagrams. If p £ K*V->>®, then the diagram is + Hi(G,p) Hi(G//T) * dG KE{& KE(G//T) d, G//^ U * KV^°/Kp KV(G//T),0 0, 131 and the exact sequence of kernels is 0 -+ H!(r,p) - KE^ Kv™/Kp % -+ 0. If p ^ K ( >'®, then the diagram is 0 0 • Hx{G,p) . H^GI/TMp)) • KE™ —^ • KVM>°/Kp •0 — KEW'T) ^ KVWM>°/Kn(p) . 0, and the exact sequence of kernels is 0->Hl(r)^KEM^Kv™^0. n Proposition 4.3.6 (Contracting a Contractible Subgraph). Suppose a forest T is a subgraph of G. If'h\(G) > 0, then *G(A)\AE(rro Letp be an element of Kv^'° = *G//r(A)- and p £ KV(T^°. ^G,p(A)\AE{r)=0 = Then *G//T,n(p)(A)- Proof The projections of HY(G) and # i ( G , p ) onto i ^ ( G / / r ) are Hi(G//T) and H\{G/ /T, 7r(p)), respectively (Lemma 4.3.5). The kernels of both projections are Hi(T) = 0, so the formulas follow from Lemma 3.2.4 for projecting a configuration. • Remark 4.3.7. Consider a forest T that is a subgraph of G as in Proposition 4.3.6, but 132 suppose p e KV(T),0^ A Spiitting a . Hi(G//T) ->• Hi{G,p) of the exact sequence o - # i ( i » -> HX{G,P) - #i(G//r) -, o will provide a direct sum decomposition H1(G,p)^H1(T,p)®a(H1(G//T)). Without additional assumptions, it is impossible to guarantee a splitting which does not meet T, i.e., in general, there is not a splitting a : Hi(G//T) -> Hx{G,p) D E K E { T ^- \ Therefore, the hypotheses of Lemma 3.1.10 are not satisfied for this direct sum decomposition, and the second graph polynomial will not be a product in general. In the case that the contractible subgraph V is a single edge, Proposition 4.3.6 states Corollary 4.3.8 (Contract an Edge). Suppose e is an edge of a graph G that is not a loop. If hi(G) > 0, then *G(A)\Ae=0 Let p be a nonzero element ofKv(G^°. = VG/e(A). Ifp^Kv^°, then ^GAA)\Ae=0 = ^G/eMpMy Corollary 4.3.9 (Isolate Nontrivial Edge). Suppose G is a graph and e an edge that is in a circuit but not a loop. If h\{G — e) > 0, then ^G(A) = AeVG_e(A) + VG/e(A). 133 Ifp is a nonzero element of KV(G^° ^G,p(A) and p £ Kv<"e>'°, then = Ae^G^p(A) + qG/eAp)(A). Proof. The variable Ae has degree at most 1 in $Q, SO the graph polynomials may be written as mG(A) ^GM) = AeF + G = AeH + I. Then F and H are determined to be ^G~e anc ' ^G-e,p by differentiating with respect to Ae using Proposition 4.3.4. Similarly, G and / are determined to be ^ G/e and ^G/e n(p) ^ setting Ae = 0 using Proposition 4.3.6. For a combinatorial argument, partition the set of spanning forests into those containing e and those not containing e. A spanning forest T does not contain e if and only if T is a spanning forest for G — e, and a spanning forest T contains e if and only if T/e is a spanning tree for G/e. Similarly for the second polynomial, a two-forest T does not contain e if and only if T is a two-forest for G — e (the possibility that T is not a two-forest for G — e as in Proposition 4.3.4 is excluded by the assumption that e is in a circuit). A two-forest T contains e if and only if T/e is a two-forest for G/e. In addition, the value of s G/e~T/e(7r(p)) m e a c n case SG~T(P) is equal to SQ-T-e(p) > which is straightforward from the definitions. or • P r o p o s i t i o n 4.3.10 (Disjoint Union). Suppose G is the disjoint union of two subgraphs G\ and G<i, and suppose h\(G2) > 0. If h\{G\) *G(A) = Ifp G K \ >- is a nonzero element of K *GM^G2(A). '• l)' , then ^GAA)-^G1AA)^G2(A). > 0, then 134 In particular, the graph hypersurfaces of G are joins of graph hypersurfaces in the disjoint linear subspaces P in P K contained ). Proof. The homology of a disjoint union is the direct sum of the homologies of the components. Thus, the configuration H\(G) configurations Hi{Gi) C K E ^ \ Hi(Gi,p) because p has a preimage in K C KE Similarly, Hi(G,p) © Hi(G2) C KE{-G^ is the direct sum of two is the direct sum © KE{-G^ ^ l>. According to Lemma 3.1.10, the configuration polynomial of the direct sum is the product of the configuration polynomials for each factor. Combinatorially, the bijection of the spanning forests of G with the pairs of spanning forests of G\ and G2 proves the formula for the first graph polynomial. For the second graph polynomial, the two-forests of G correspond to a pair of a spanning forest in one G{ and a two-forest in the other. The assumption that p (E guarantees that SQ2_p(p) K*(Gi)fi = 0 for all two-forests F in G2) so only pairs of two-forests in G\ and spanning forests in G2 contribute. • There is also a formula for the second graph polynomial of a disjoint union for all nonzero p G Kv^>^: P r o p o s i t i o n 4.3.11 (Disjoint Union, General Momenta). Suppose the graph G is the disjoint union of G\ and G2, h\(Gj) element of K is nonzero for both i, and p is a nonzero ~' ' ' . Let pi be the image of p under the projection from KV(G),0 its subspace K \ i'^. *G,P(A) If both pi are nonzero, = *Gl,Pl(A)yG2(A) to then + *Gl(A)*G2,P2(A). If either Pi is zero, a formula for ^ ^ ^ ( ^ 1 ) is given in Proposition (4.5) 4-3.10. 135 Proof. Combinatorially, the two-forests in G correspond to a pair of a spanning forest in one G{ and a two-forest in the other. If F is a two-forest in G\, then = 3GI-F(P) SGi-F(Pl) because each component C of G2 has veV{C) by definition of KV^G'^. The same holds for two-forests of G2, which completes the combinatorial proof by the general combinatorial formula for the second graph polynomial (Proposition 4.2.12). For an algebraic argument, use the direct sum decomposition KV(G),0 = KV{Gx)fi under which p = p\ + P2- There are qi e K 0 KV(G2),0 ^i> for which dG(qi) — pi for both i. By the multilinearity of the determinant, *G)P(^) = d e t £ £ | # l ( G - p ) = ^BE\H1(G,PI) +AQXBE\HX{G,P2)- Each Pi satisfies the condition in Proposition 4.3.10 for one of the G{, so detBE\Hl{Gpi) +detBE\Hl(G)P2) = *GliPl(A)VG2(A)+ *Gl(A)VG2>P2(A). • P r o p o s i t i o n 4.3.12 (Vertex Union). Suppose G is the union of two subgraphs G\ and G2 that intersect at only one vertex and no edges. Suppose that h\{G2) > 0 so 136 that its first graph polynomial is defined. If h\{Gi) > 0, then yG(A) = If p is a nonzero element of KV(G^°, *Gl(A)*G2(A). then Proof. The proof for Proposition 4.3.10 works here, as well. The intersection Gi nG2 is a vertex, so h\(G\ n G2) = 0 and j^(GinG 2 ),0 = Q. A Mayer-Vietoris argument provides the direct sum decompositions #l(G0 = # i ( G i ) © # i ( G 2 ) and Hi(G,p) = H^G^p) © #i(G2). D Corollary 4.3.13 (Loops). Suppose an edge e is a loop in a graph G, suppose h\(G — e) > 0, and suppose p is a nonzero element of K '>> Then VG{A) = Ae*G_e(A) arid VG)P(A) = Ae*G_ejP(A). Proof. Apply Proposition 4.3.12 with G\ = G — e, G<i = e, and use ^e(A) = Ae for a loop e. • Corollary 4.3.14 (Vertex Union, More General Momenta). Suppose G is the union of two subgraphs G\ and G 2 that intersect at only one vertex and no edges. Let px be a nonzero element of Kv^-i>$ fori = 1,2, and consider p = p\ + p2 £ Then *GAA) = 9GlJ}1(A)9G2(A) + ^Gl(A)^G^2(A). KV^),Q. 137 Proof. The same proof as for the disjoint union (Proposition 4.3.11) applies here. Note the difference between the two results is that part of the hypothesis of this corollary is that p decomposes into p\ and P2- In the disjoint union case, p automatically decomposes into p\ and P2- It i s necessary in either case to assume both Pl are nonzero for the second graph polynomials to be defined. • P r o p o s i t i o n 4.3.15 (Trivial Edge). Let G be a graph and e an edge not contained in a circuit. If h\(G) > 0. *G(A) = *G~e(A) = *G/e(A). If p is a nonzero element of *G,p(A) = *G-e,p{A) = *G/eMp)(A). Remark 4.3.16. When e is not contained in a circuit, G — e is a disjoint union of two graphs and G/e is a vertex union as in Proposition 4.3.12, so the graph polynomials decompose further. Even though the sets V(G — e) and V(G) are the same, the v G e is not the same as p G if^( G )>° because G — e has more assumption p <E K ^ ~ >^ components than G. Proof. The condition that e is not in a circuit implies that the connected component containing it becomes two components m G — e by Lemma 4.3.3. Contracting e in G joins those two components at the vertex to which e is contracted. The hypothesis that e is not in a circuit implies that Xe\jj £ (Lemma 4.3.2), so H^G) E to K ^- e is Hi(G-e) G e /Q\ is identically zero C K ( ) ^ . The restriction of the configuration (Lemma 4.3.1), so HX{G) = Hi(G-e), polynomials are the same: *G(A) = *G-e(A). In particular, these polynomials do not depend on Ae, so *G(A) = *G(A)\Ae=0 = VG/e(A) Hi(G) and the first graph 138 by Proposition 4.3.6. If p e Kv(G~e^°} then it has a lift q e KE^G^e, zero (Lemma 4.3.2). Therefore, H\{G,p) so Xe\H^G^ is also identically is contained in KE^~e, so its restriction to this subspace is itself, but it is also H\{G - e,p) by Lemma 4.3.1. Therefore, as long as p is nonzero, the second graph polynomials are defined and ^G^A) = ^G-e,piA)- In particular, these polynomials do not depend on Ae, so ®G,p(A) = ^G,p(A)\Ae=0 *G/e,n(p)(A) = by Proposition 4.3.6. • P r o p o s i t i o n 4.3.17 (Valence Two Vertex). Suppose edges e\ and e2 meet at a valence 2 vertex and are contained in at least one circuit. VG(A) = (Al + A2)*G-ei(A) + Then *G/eie2(A). Proof. Every spanning forest must contain one or both of e\ and e2 in order to include the vertex where they meet. Partitioning the sum in the graph polynomial by those spanning forests containing just e\, just e2, or both e\ and e2, the graph polynomial is VG(A) = A2VG_e2(A) + A^G_ei(A) + ^>G/eie2{A). In fact, every spanning forest T that does not contain ei gives a unique spanning forest (T — e2) U ei that does not contain e2. Therefore, ^G~eM) and the formula is verified. = ^>G-eM)i • 139 Proposition 4.3.18 (Parallel Edges). Suppose edges e\ and e2 have the same pair of distinct vertices, and suppose h\{G — e\) > 0. Then *G(A) = AlA2^G^ei_e2{A) + A^{G_ei)/e2{A) + A2*G/ei_e2(A). Proof. The pair of edges form a circuit, so they are not both contained in a spanning forest. Thus, every spanning forest contains neither edge or only one edge. If F contains neither edge, then F is a spanning forest of G — e\ ~ e2, which corresponds to the first term on the right side of the equality. If F contains ej but not ej, then F/ei is a spanning forest of (G — ej)/e^, which gives the last two terms. • Proposition 4.3.19 (Edge Union). Suppose G is the union of two subgraphs G\ and G2 that meet at exactly one edge e, riot a loop. Suppose that h\(G{ — e) > 0 for both i = 1,2. Then *G(A) = Ae (*Gl-e(A)*G2/e{A) + v& G l / e (A)^ G 2 „ e (A)) + *Gl/e(A)*G2/e(A). In particular, *G-e(A) = *Gl-e(A)yG2/e(A) + 9Gl/e(A)*G2_e(A). (4.6) Proof. It suffices to consider G to be the connected component containing e because the other components can be factored out of the equation using Proposition 4.3.10. The decomposition of Corollary 4.3.9 applies to give VG(A) = Ae^G_e(A) + tfG/e(A). The graph G/e is the vertex union of G i / e and G2/e in the sense of Proposition 4.3.12, so *G/e(^) = S'd/eC^Ga/eW The spanning trees of G — e correspond to the spanning trees of G not containing e. Let T be a spanning tree of G not containing e, and let T{ = Gj D T for i = 1, 2. 140 As subgraphs of a tree, the X-L are forests. Also, T\ flT2 is a pair of disjoint vertices, the vertices of e. The Euler characteristic of a forest is the number of components, so the Mayer-Vietoris relationship among the Euler characteristics is h0(Tl) + h0(T2)=X(T1) + x(T2) = x(T) + 2 = h0(T) + 2 = 3. Therefore, exactly one of the Tj has two components, and the other has one. Suppose T\ has one component and T2 two components. Note that both G\ — e and G2 — e are connected because e is contained in a circuit of G\ and of G2 (Lemma 4.3.3). The spanning tree T contains all the vertices of G (Lemma 4.1.9), so the forests Tj contain all the vertices of Gj. Therefore, T\ is a tree with all vertices of G\ — e and, thus, a spanning tree of G\ — e (Lemma 4.1.13). The forest T2 has two components and all vertices of G2 — e and, thus, a two-tree of G2 — e (Lemma 4.1.16). To conclude, I need to show that T2 U e is a spanning tree of G2, and thus T2 U e/e is a spanning tree of G2/e. By adding the edge e to T, h\{T U e) = 1 (Lemma 4.1.6), and for the same reason, h\(T\ U e) = 1. If h\(T2 Ue) = 1, then T U e will contain two circuits, one in G\ and one in G2, contrary to h\{T U e) = 1. Therefore, h\(T2\Je) — 0, thus ho(T2Ue) = 1 (Lemma 4.1.6), and T2Ue is a spanning tree of G2 as claimed. • Example 4.3.20 (A Banana, Degenerate Case). Consider the graph G in Figure 4.8. Each edge is a spanning tree, so *G{A) =AX+A2 and For both i = 1, 2, j ^ i, 1. dA^G(A) = 1, 2. dA^G^A) = *G_ehP{A)=p\Aj, $>GiP{A) = pJAiA2. 141 v Figure 4.8: A circuit with two edges; a banana. ei Ae ei Figure 4.9: Two banana graph. The graphs G — ej have /&i(G — ej) = 0, so the graph polynomial is not defined. For consistency among the formulas, it would seem that ^G~e- (^) = 1 would be a good convention, and this would follow by defining the configuration polynomial of the 0 configuration to be a constant (1 for graphs because of the integral basis). There is no problem defining ^Q^e.p(A) because H\(G,p) p, but there is a problem defining ^Q/e. P{A). is always nonzero for nonzero A graph with a single vertex has no nonzero momentum and no two-forests, which suggests defining the second graph polynomial to be identically 0 if p = 0. This convention fits the combinatorial formula (Proposition 4.2.12), but it violates the configuration definition as the configuration polynomial of H\(G, 0) = H\{G), which is nonzero for h\{G) ^ 0. Example 4.3.21 (Gluing Bananas, Degenerate Case). Let G be the graph in Figure 4.8 142 as in the previous example. Suppose G is identical to the graph G in Figure 4.8, but all labels and variables are denoted with the accent. Identifying edge e2 with e2 in these two graphs yields the graph Y in Figure 4.9. Letting the identified edge be e with corresponding variable Ae, the graph polynomials are i£ r = A1A1 + AiAe + A\Ae and *r^ = p\A\A\Ae. In particular, if I follow the suggested convention above that the first graph polynomial be 1 for acyclic graphs, ^ r = Ae(Ai +Ai) + = M*G/e2*G-e2 AiAi + *G-e2*G/e-2) + ^G/e^G/e2 In other words, this convention would agree with the result of Proposition 4.3.19. Moreover, F — e is also a graph isomorphic to G and G but with different labels, and * r - c = *G/e2*G_e2 + *G-e2*G/e2In general, gluing a graph G\ to the banana graph G along an edge to produce a new graph Y and then deleting that edge as in Proposition 4.3.19 produces T — e, a graph isomorphic to G\. Example 4.3.22 (Adding a Bubble). Let G be the graph in Figure 4.10. Gluing G to another graph Y along an edge e and then deleting e produces a graph that is F with a bubble on edge e. For example, let Y be the graph in Figure 4.9. This operation produces the graph in Figure 4.11. To demonstrate some propositions of this section, I compute the polynomial of "adding a bubble" following two methods. First, hit it with the hammer of Proposition 4.3.19 assuming h\(Y — e) > 0 so the first graph polynomials are well-defined. Note that ^G-e = Ai + A2 and ^G/e = AXA2 + AtA3 + A2A3. 143 Figure 4.10: Two bananas, one edge split. Then the bubbled graph H has graph polynomial *H = ^G/e^T-e + ^G-e^T/e (AXA2 + AiA3 + A2A3)VT_e + (Ai + A2)VT/e As a second method, put the bubble on V by a sequence of two operations: subdivide e into e2 and e%, then attach an edge e\ parallel to e2. Initially, q?r = ApV e ^ r „ e + ^^Tr / e - Let F be the graph F with e subdivided into e2 and e3. The formula for a valence two vertex (Proposition 4.3.17) shows that the graph polynomial of F is VF = (A2 + A3)Vr_e + VT/e. Note that e% is trivial in F — e2 in the sense of Proposition 4.3.15, so ^JF_e^ = $Y- 144 Figure 4.11: The graphs of Figures 4.9 and 4.10 glued along e, then with e removed to create a bubble on edge e% of the two banana graph. Then the formula for parallel edges (Proposition 4.3.18) shows qH = A1A2^H-ei-e2 + A^{H_ei)/e2 + = AlA2^F^e2 + A^F/e2 + = A1A2^F_e2 + ^i^rUe=^3 + = AiA2Vr_e + (Ai + A2)(A3Vr_e = {AiA2 + AjA3 + A2A3)VT_e A2VH/ei_e2 A2^F/e2 A2*r\Ae=A3 + + (^1 + tfr/e) M)^T/e- Remark 4.3.23. Suppose I glue G\ and G2 along an edge e and then remove it, as in Proposition 4.3.19, to produce a new graph G. If both G{ are log-divergent, i.e., 2hi(Gi) = \E(Gi)\, then | E(G - e) | = | E(Gi - e) | + | E{G2 - e) | = 2h1(G1) + 2h1{G2) - 2. The value of h\(G — e) can be computed by the following Mayer-Vietoris sequence: 0 -+Hi(Gi - e) © Hl(G2 - e)-> H^G - e) -> HQ((Gi - e) n (G2 - e)) -+ # 0 ( G i - e) © H0(G2 - e) ^ H0(G - e) -> 0. 145 Note that {G\ - e) n (G2 - e) is a pair of disjoint vertices. Therefore, / i l ( G - e ) = /ii(Gi) + / i i ( G 2 ) - l , so G—e is log-divergent, too. The log-divergent graphs play a central role in quantum field theory, and this operation is a simple way to produce log-divergent graphs from log-divergent graphs. Note that the log-divergent condition 2h\(G) = \E(G) | is a strong condition, and finding examples becomes more difficult as /11(G) increases. In [Blo07], the graph motive of a graph G with n edges is defined as M(G) ^n-l(Pn-l _ XGJ q(n _ ^y = ideally, one would like to understand the polynomial formulas in the preceding propositions in terms of the corresponding geometric and motivic objects. For example, T h e o r e m 4.3.24 ( [BloOT]). If G is the union of two graphs G\ and G2 that are either disjoint or intersect at a single vertex, M(G) = M(Gi) then ® M(G2). I am particularly interested in a motivic interpretation of Equation (4.6) in Proposition 4.3.19, but I do not know one. That equation, partitions the variables into two sets based on whether the edges are in G\ or G2, and it would be wonderful to be able to relate the motives for Gj, G^/e, and Gj — e to the motive for G — e. In general, suppose F and G are homogenenous polynomials of degrees / and g in some set of variables denoted A. Let $ and T be homogeneous polynomials of degree ip and 7 in another set of variables B. Suppose that fi = <pg 146 so that FT + $ G is homogeneous in the set of both variables. Suppose that the variables A are the linear forms on a vector space W, and the variables B are the linear forms on another vector space V. The analysis of the motive for Equation (4.6) would benefit from a description of the geometry and topology of the hypersurface defined by FT + $ G = 0 in F(W © V). Moreover, this general situation could be applied to understanding the graph hypersurface of other graph operations such as the second graph hypersurface of a disjoint union, Equation (4.5). 4.4 Quadratic Linear Series Defined by Graphs Let G be a graph. Each edge e determines a rank at most one quadratic form Xg on H\(G) and H\(G,p) Let E = { e i , . . . ,en}, by taking its dual on K and let Xf Qc,p similarly on F(Hi(G,p)). Define QG : P ( # i ( G ) ) ->• F(KE) = X\. the map that takes a line [x] in Hi(G) , squaring it, and restricting it. to [Xf(x) E : • • • : X%(x)] in F(K ). to be Define In other words, each graph comes with a naturally defined quadratic linear series as defined in Section 2.2. The quadratic linear series is base-point free because H\{G) and H\(G,p) are configurations in KE. Namely, a nonzero element h can be written uniquely as h — y^ hee eeE in the basis E with at least one nonzero coordinate he. nonzero coordinate for Qciih]) in Then X^ih) = hi is a F(KE). Intuitively, more edges will make the maps QQ and QQ p more likely to separate points, but the combinatorics and topology of the graphs put restrictions on the relationship between the number of edges and the first Betti number so that often there cannot be enough edges to separate points. The main result of this section is T h e o r e m 4 . 4 . 1 . Let G be a connected graph and p a nonzero element of KV^',Q. If either map QQ or Qc,p is infective, then h\{G) < 4. In particular, if either map is an embedding, then h\(G) < 4. 147 In particular, QQ is often not an embedding. In Section 2.2, I discussed how the incidence variety for constructing the dual of the image of QQ will provide a resolution of singularities for the dual if QQ is an embedding. Proposition 2.2.2 proves that the dual is the graph hypersurface XQ. This approach is suggested in [BEK06]. The same is true for the second graph hypersurfaces YQP, but there is less interest in finding a resolution of singularities for YQP because the Feynman integrals (4.3) are periods for the complement of XQ. The implication of Theorem 4.4.1 is that the incidence variety may not provide a resolution of singularities for the graph hypersurfaces. Theorem 4.4.1 and an application of a theorem of Fulton and Hansen (Theorem 2.2.7) provide a simple criterion to rule out whether QQ or QQP is an immersion. Corollary 4.4.2. Let G be a connected graph and p a nonzero element of K ^ '^. If h\(G) > 4 and h\(G) > \ V(G) \, then QQ is not an immersion. If h\{G) > 4 and h\(G) > | V(G) | — 2, then QQ,P is not an immersion. Proof. Theorem 4.4.1 proves that QQ and QQJP are not embeddings when h\ (G) > 4. The theorem of Fulton and Hansen (Theorem 2.2.7) gives a simple criterion for QQ and QQ^P to be embeddings assuming they are immersions satisfying certain dimension conditions. For example, Theorem 2.2.7 applied to QQ states that if 2dimP(#i(G)) = 2 ( / i i ( G 0 - l ) > I E{G) \ - 1 = dimP (KE^GA and QQ is an immersion, then QQ is a closed embedding. The Euler characteristic (4.1) may be used to express the inequality more simply 2{hi{G)-\) > \E(G)\-1 = \V(G)\ + hi(G) > \V{G)\. hi(G)-l-l, 148 Figure 4.12: Two banana graph, revisited. Similarly, if h\{G) > \ V(G) | — 2 and Qc,p 1S a n immersion, then QQP is an embed- ding. Combining the dimension conditions from both theorems, it is impossible for the maps to be immersions. • Before proving Theorem 4.4.1, it is useful to study some examples where QQ is an embedding. The first exceptional case to note is when h\(G) QQ is an embedding of a point P° in F(K^). is 1: the map Next I provide examples when h\(G) is 2, 3, and 4 for which QQ is an embedding. In the search for graphs providing an embedding, there are two key lemmas that are useful for excluding particular examples besides Theorem 4.4.1. Lemma 4.4.9 explains that it is unnecessary to consider graphs containing vertices of valence one or two. Lemma 4.4.6 shows that circuits must have at least h\{G) edges. These conditions are quite restrictive. In the following examples, each graph has a circuit basis in which each pair of basis circuits shares an edge and the basis circuits satisfy the last part of Lemma 4.1.24. I am not invoking the lemma; I am constructing explicit bases with those properties. After listing the examples, I will explain for all three simultaneously why the quadratic linear series is an embedding. Example 4.4.3 (Two Bananas, h\(G) = 2). The graph G has two vertices connected by three edges e\, e2, and e% as in Figure 4.12. Choose the circuits i\ = e\ + e% and ^2 = e 2 + e 3- I have used the arrows to indicate the head of each edge to compute H\{G). Example They are not intrinsic data of the graph. 4AA (A Wheel with Three Spokes, h\(G) — 3). Let G be the complete 149 vi Figure 4.13: A wheel with three spokes, graph on 4 vertices as in Figure 4.13. Choose the circuit basis h = e l + e 5 - e4^2 = e 2 + e 6 - e 5 h = e3 + e4 - e 6 . Example 4.4.5 (Complete Bipartite Graph /C33, h\(G) = 4). Let G be the complete bipartite graph on 3 and 3 vertices pictured in Figure 4.14. Choose the circuit basis h = e l - e6 + e9 - e5 £2 = ^2 ~ ^7 + e 9 - e ^3 = e3 - e 7 + e 9 - e 5 £4 = e 4 - e 6 + eg - e 8 8 To show that the maps QQ are embeddings for these examples, I note the following properties of the bases I have chosen. (a) Each basis circuit has an edge that is in no other basis circuits, and (b) each edge that is not unique to one circuit is in exactly two circuits, with the exception of edge e§ in Example 4.4.5. 150 Figure 4.14: A complete bipartite graph, ^ 3 3 . The exception in (b) prevents me from providing a one-size-fits-all explanation that all three cases are embeddings, but the proof follows similar steps for each case. Suppose that QQ(X) = QG(V) m an y °f the examples. By property (a), I may assume the first /11(G) quadratic forms are X^,..., Xi C such that xj — Cyf for all i, hence Xj = ±\/Cy^. ^ > . Thus, there is a constant Because these are projective coordinates, I can ignore the \/~C'. In the first two examples, each of the other edges in the graph is shared by two circuits as in property (b). forces (±£/j)(±t/j) = ViVj- If e is shared by £j and lj, then X^(x) Thus ±'(/j and ±yj (x'l, . . ., x^.(G)) = ± ( y i , • • • iVh-iiG))-! which ls = must have the same sign. CX%(y) Thus the same projective point. In the third example, the same reasoning holds except that all circuits share e§, which belongs to all circuits. To avoid this difficulty, use the other edges first. In particular, use e2 to establish that ±y\ and ±2/3 have the same sign, e^ to establish that ± y i and ±2/4 have the same sign, eg to establish that ±2/2 same sign, and eg to establish that ±2/2 an an d i?/3 have the d ±2/4 have the same sign. Then use e§ to see that ±yi and ±7/2 have the same sign. 151 It may appear that some of this information is redundant, but I have assumed for convenience that 0 has both signs. Thus, I need all of the relations above. For example, if 2/3 = 0, knowing that ±yi and ±y2 have the same sign as ±y% is not sufficient to conclude that ±y\ and ±y2 have the same sign. These examples are also immersions. This fact can be checked by direction computation of the Jacobians locally. Properties (a) and (b) of the examples makes this computation similar in each case, so I carry out the case for Example 4.4.5. First I write the quadratic forms X? in the basis {L{\ of Hi(G)v dual to the basis {£j} given above: X\ = L\ X2 = L2 Xl = L\ X^ — L 4 Xb = Lx + L3 + 2L1L3 XQ = LI + L^ + 2L\L/[ X7 = L2 + L 3 + 2L 2 L 3 Xg = L2 + £4 + 2L2L^ 3 i<3 Consider the affine patch L\ ^ 0; the computation is the same for all L{ 7^ 0 by the symmetry of the graph. Normalize to local coordinates ou = j ^ - , j 7^ 1, and let q^ 152 be Xf in these local coordinates. The Jacobian is 0 0 0 2a2 0 0 0 2a3 0 0 0 2(24 0 2a3 + 2 0 0 0 2a4 + 2 2a2 + 2a3 2a2 + 2a3 0 2a2 + 2a4 2a2 + 2a4 0 \ 2a 2 + 2a 3 + 2a 4 + 2 2a 2 + 2a 3 + 2a 4 + 2 2a 2 + 2a 3 + 2a 4 + 2 / The second, third, and fourth rows show that this is injective when 0,2,0,3, a,\ ^ 0. Even when a 3 and 124 are zero, rows five and six maintain injectivity. There would be a problem if 2<22 = 0, 2a2 + 2a 3 + 2*24 + 2 = 0, 2<22 + 2a 3 = 0, and 2<22 + 204 = 0, but there is no solution to this system of equations. Now I proceed to proving Theorem 4.4.1. To simplify the proof, I restrict to connected graphs as each component determines a map between projective spaces. Some of the facts that I prove below require h\{G) > 1, which is strong enough for Theorem 4.4.1 The proof requires some features of the combinatorics of the graph G. To connect the combinatorics of the graph with the configuration H\{G), I use circuit bases as defined in Lemma 4.1.24. The girth of a graph is the smallest number of edges in a circuit. L e m m a 4.4.6 (hi(G) Bounds Girth.). Consider QG : P ( F i ( G ) ) -> ¥{KE) a field K of characteristic injective, not two. If h\{G) is grea,ter than 1 and the map QQ is then the girth of G must be at least h\{G). a line in H\{G,p), so if Qc,p must be at least h\(G) zs injective, when h\[G) > 1. over Every line in H\(G) is also then so is QQ, and again the girth of G 153 Remark 4.4.7. Using Lemma 4.1.24, it is easy to find examples in characteristic two that are injective but do not satisfy the girth condition. For the proof, I need b\ 7^ —b\ for nonzero b\ G K. For the remainder of this section, assume char(K) ^ 2. The idea for the proof is that if a basis circuit t\ has fewer than h\{G) edges, then I can find [b2 : • • • : b^ /£)] £ P ( i / i ( G — ei)) such that QG (\h : b2 : • • • : bhl(G))) = QG ( h & l • b2 : • • • : bhl(G)}) for any b\. In particular, since not all b2, • • . , ^ W G ) are zero, any nonzero b\ will provide two points mapping to the same point via QQ. Proof. Suppose £\ is a circuit with fewer than h\{G) edges. I show that QQ is not injective. Complete {£\} to a circuit basis {£\,... }£fl,/Q\} for H\(G) by Lemma 4.1.24. For each edge e, consider when X2e (bi,b2,...,bhl(G)) =X2e (-bi,b2,---,bhl(G)) • Let hi(G) 2 X e {bX,b2, • • • , ^ ( G ) ) = J ] a e,ifo? + Yl Ce,i]bibJ- i=\ i<j and ae_j = X e (£ v ;) 2 . Then In particular, each c e ^ = Xe(£i)Xe(£j) Xl [bi,b2,...,bhl{G)j =X^-b1,b2,...,bhi(G^ if and only if hx(G) 2 Yl Factoring out the b\, this is 2b\ Y2j=2 c e,ljblbj c e,ljbj = 0. = 0. Let MG) £ e (&2,---,&fc1(G)) = 5 Z c e,lj fe jJ'=2 154 Let He be the hyperplane in W(H\(G — e)) defined by the linear form Le. I will show that the girth assumption implies that there is a projective point [fy? : • • • : fyii(G)] m ^he intersection f]eejr;He. If e ^ £1, then Xe{i\) = 0, so ce^j = 0 for all j and Le(b) = 0. Thus f]He= eeE f]He. ee£\ If e G £i, then Le(6) may be nonzero. However, according to the form of the circuit basis in the last part of Lemma 4.1.24, I may assume X^(b) = bf, so Lei(b) = 0. If the circuit l\ has at most h\{G) — 1 edges, then there are at most h\(G) — 2 linear forms Le(b) that are nonzero. In particular, Heefi ^e *s ^ n e intersection of at most h\{G) — 2 hyperplanes in ¥(H\(G — e\)). Therefore, the dimension of Heefi -^e is at least 0; that is, there is a point [62 : • • • : b^ IQ\] e Pleefi ^e< Pick any nonzero 61. Then QG {[h : 62 = • • ' = ^ ( G ) ] ) = <?G ( h b l : &2 = ' ' ' = ^ ^ G ) ] ) > and [61 : 62 : " '" : fyji (G)l ^s n o t ec ua l l t o [—&1 : ^2 : "' " : bh iG\}. • Lemma 4.4.6 shows that injectivity of QQ puts a restriction on the girth of the graph. The next theorem uses the girth and valence of the graph to give a lower bound on the number of vertices. The valence of a vertex is the number of incident edges (an edge with a single vertex, i.e., a loop, counts twice). The valence of a graph is the minimum valence of its vertices. This theorem is in many texts that discuss extremal graph theory, for example [Lov93] from which the proof below is adapted. The result originally is due to Erdos and Sachs [ES63], I provide the proof for the readers who are not graph theorists. Theorem 4.4.8 (Erdos-Sachs). / / G has girth g and every vertex has valence at least k, then I V(G) \>l + k + k(k-l) + --- + k{k~l)^r for g odd 155 and | V(G) | > 2 ( l + (k - 1) + • • • + (k - l ) ^ " 1 ) for g even. Proof. In the following proof, I use the word connected in the graph-theoretic sense that two vertices in a graph are connected if there is an edge in the graph with those vertices. In particular, I am not using it in a topological sense. First suppose g is odd. Pick a vertex v in V(G). Let Si be the number of vertices a distance i from v. Note that | SQ \ = 1 and \S\\ > k. I claim that each vertex in Sj_i connects to at least (k — 1) vertices in Si for i = 2,..., y~Y~ J a n d thus |3|>(fc-l).|$_l| for i = 2 , . . . , ^—. Each vertex in S j - i is connected to at least one vertex in Sj_2 for i > 2 by definition of Sj-i- If a vertex x in S?;_i is connected to two vertices in 5^-2, then there will be two paths from v to x of length 2 — 1, giving a circuit of length at most 2z — 2. In particular, each vertex in Sj_i must only be connected to only one vertex in Sj„2 f° r ^ < f + !• If two vertices in Sj_i are connected, then there will be a circuit of length at most 2z — 1. In particular, the vertices in Sj-i are not connected if i < ^—. Therefore, when o+ l o each vertex in S^_i is connected to one vertex in Sj_2 an d no vertices in Sj_i. Because g is odd, the maximum value of i in that range is ^—. To satisfy the valence condition, each vertex in Sj„i must connect to (k — 1) vertices in Si for i = 2,..., ^2", and |$|>(fc-l)l$-i| for such z. Therefore, ^ ( C ) | > | So | + | Si | + • • • + > l + k + k(k-!) + •••+ k(k- 1)V. 156 If g is even, start by picking a pair of adjacent vertices v and w. Let S{ be the number of vertices a distance i from the the pair {v, w}. Note that | SQ \ — 2. I claim that each vertex in 5"j_i connects to at least (k — 1) vertices in Sj for i = l , . . . , | — 1, and thus I St | > (k - 1) • | 5 , _ ! | for i = 2 , . . . , | — 1. Each vertex in <S^_i is connected to at least one vertex in S*j_2 for i > 2 by definition of S^-i- If a vertex x in Sj_i is connected to two vertices in Si-2 for i > 2, then there will be two paths from {v,w} to x giving a circuit of length at most 2i — 1. In particular, each vertex in 5j_i is connected to exactly one vertex in S{^2 f° r 2 < i < ^—. If a two vertices in 5j_i are connected for i > 2, then there will be a circuit of length at most 2z. In particular, the vertices in S{-i are not connected if 2 < z < | . Therefore, when o 9+ 1 2< i< - < , 2 2 ' each vertex in S^-i is connected to one vertex in Si-2 ari( i n0 vertices in 5^-1. Because (7 is even, the maximum value of i in that range is 5-2 ^ Si_l = ^o is the pair of connected vertices {v,w}. 1. When i = 1, To satisfy the valence condition, each vertex in Si-i must connect to (k — 1) vertices in Si for i = 1 , . . . , | — 1, and ISil^fc-i)!^! for such i. Therefore, V(G) I > I SQ I + I Si I • > 2 ( l + (fc-l) (fc Si 1)2 + - + (fc-i)§-i; n Next I show that determining whether QQ is injective only needs to be answered for graphs whose vertices have valence at least 3. First, I mention some terminology. 157 Contraction of an edge of a graph is defined to be the removal of an edge with distinct endpoints followed by identification of its endpoints. That is, contract the edge in a topological sense. A contraction of a graph is a series of contractions of edges in the graph (order does not matter). L e m m a 4.4.9 (Contract to Valence at least 3). If every component T of a graph G has hi(F) > 1 (in particular contraction G' with the if G is connected with h\(G) > I), then G has a properties (a) every vertex of G' has valence at least 3, (b) QQ is mjective if and only if QQ/ is infective (c) h1(G) = Remark h1(G') 4.4.10. The proof below is simple: contract edges with "valence" less than 3 (obviously, vertices, not edges, have valence; this is clear in the proof). The key conclusions of the lemma are parts (a) and (b); part (c) is true for any contraction. The hypothesis that h\(G) > 1 is necessary to conclude (a). As mentioned at the beginning, in case h\{G) — 1, QQ is always injective. Proof. If a vertex v in G has valence one, contract its incident edge. If a vertex v has valence two and two distinct incident edges, contract one of its incident edges. There will not be valence two vertices with one incident edge (i.e., loops) because I have assumed that no components T of G have h\(T) = 1. Carrying out all such contractions yields a graph G' whose vertices have valence at least 3, so (a) is satisfied. An edge e incident to a valence one vertex will not be in a circuit (Lemma 4.1.19), so Xe induces the zero functional on H\(G) (Lemma 4.3.2). If edges e\ and e2 meet at a valence two vertex, then every circuit containing one of these edges must contain the other (Lemma 4.1.19). X\\JJ /Q\ — ±X2\JJ,(Q\, so X\ Depending on the orientation of the edges, = Ar| as quadratic forms on H\(G). In particu- lar, the contractions to get G' do not change the injectivity of QQ, SO (b) is satisfied. As already noted, every contraction G' of G will have h\{G) = h\(G'). • 158 Now that I can restrict to connected graphs whose vertices have valence at least 3, the Euler characteristic (4.1) gives a useful upper bound on the number of vertices of the graph. Lemma 4.4.11 (Bound on V(G)). A connected graph with valence at least 3 must have \V{G)\ < 2 M G ) - 2 . Proof. For any graph G, val u 2 | E(G) | = Yl ( ) veV(G) where val(v) is the valence of v. Because the vertices have valence at least three, 2\E(G)\ If the graph is connected, then X(G) >3\V{G)\. = 1, so the Euler characteristic (4.1) gives IIQ(G) = l-h1(G) = \V(G)\-\E(G)\ <\V(G)\-^\V(G)\ = -\\V{G)\ Therefore, \V{G)\ <2fci(G)-2. D The next proposition puts together the bounds for | V(G) | given the assumptions that I can make about the graph without losing generality as in Lemma 4.4.9. The proof of Theorem 4.4.1 follows the proposition by checking when it is possible to find a number between these bounds. 159 Proposition 4.4.12 (Requirement for Injectivity). A connected graph G with valence at least 3, with h\{G) > 1,and for which QQ or Qc,p ig infective must satisfy h](G)-3 1 + 3 + 3(2) + • • • + 3(2)—2 < | V(G) | < 2hi(G) - 2 for hx{G) odd and 2(1 + 2 + • • • + 2 " ^ l ) < | V(G) | < 2h1(G) - 2 for hx{G) even- Proof. The right-hand inequality follows from the valence of the graph as proven in Lemma 4.4.11. The left-hand inequality follows from the Erdos-Sachs Theorem 4.4.8 because the graph has valence at least 3 and must have girth at least h\{G) by Lemma 4.4.6. • Proof of Theorem 4-4-1- When h\{G) = 3 or 4, the upper and lower bounds agree in the appropriate case. Simple calculus shows that these inequalities cannot be satisfied for h\{G) > 4. In particular, the calculation goes as follows for the case when h\{G) is odd. Use the geometric sum formula to simplify the lower bound on | V(G) |: h1(G)-3 1 + 3 + 3(2) + • • • + 3(2) 2 / h^G)-! =1 +3 2 h1(G)-l Differentiating with respect to h\(G), I have 3 • 2 j 2 2 -1 ,2) °^ '. The derivative of the upper bound is 2. The lower bound increases faster than the upper bound as long as 3-2^ ^ >2. 2 Solving for hi(G), the lower bound increases faster than the upper bound when log hl{G) > - 32 log(2) ~2-37' 160 Because the lower bound equals the upper bound for h\(G) = 3 and the lower bound increases faster than the upper bound for h\(G) > 2.37, there is no odd h\(G) > 3 for which QQ is injective. The calculation goes similarly for the case when /11(G) is even. Use the geometric sum formula to simplify the lower bound on | V(G) |: h^G) 2 1 + 2 + 2 2 + --- + 2 " ^ \ l / =2 hx{G) 2~~T~ - 1 Differentiating with respect to /11(G), I have 2 • 2 2 q} ', The derivative of the upper bound is 2. The lower bound increases faster than the upper bound as long as MG) log(2) 2 - 2 ^ T " ^ ^ > 2. 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