On K-Level Matroids: Geometry and Combinatorics Francesco Grande

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On K-Level Matroids: Geometry and Combinatorics Francesco Grande On k-level matroids: geometry and combinatorics ⊕ ⊕ Dissertation zur Erlangung des akademisches Grades eines Doktors der Naturwissenschaften am Fachbereich Mathematik und Informatik der Freien Universität Berlin vorgelegt von Francesco Grande Berlin 2015 Advisor and first reviewer: Professor Dr. Raman Sanyal Second reviewer: Professor Rekha Thomas, Ph.D. Date of the defense: October 12, 2015 Summary The Theta rank of a finite point configuration V is the maximal degree nec- essary for a sum-of-squares representation of a non-negative linear function on V . This is an important invariant for polynomial optimization that is in general hard to determine. We study the Theta rank of point configu- rations via levelness, that is a discrete-geometric invariant, and completely classify the 2-level (equivalently Theta-1) configurations whose convex hull is a simple or a simplicial polytope. We consider configurations associated to the collection of bases of matroids and show that the class of matroids with bounded Theta rank or levelness is closed under taking minors. This allows us to find a characterization of matroids with bounded Theta rank or levelness in terms of forbidden minors. We give the complete (finite) list of excluded minors for Theta-1 matroids which generalize the well-known series-parallel graphs. Moreover, we char- acterize the class of Theta-1 matroids in terms of the degree of generation of the vanishing ideal and in terms of the psd rank for the associated matroid base polytope. We analyze in full detail Theta-1 matroids from a constructive perspective and discover that they are sort-closed, which allows us to determine a uni- modular triangulation of every matroid base polytope and to characterize its volume by means of permutations. A closed formula for the enumeration of Theta-1 matroids on a ground set of size n seems out of reach, but we exploit the constructive properties to provide asymptotic estimates. As a consequence, we obtain an exponential lower bound on the number of 2-level polytopes of any fixed dimension. As for the k-level matroids with k > 2, we prove that the list of excluded minors is finite for every k and we describe the excluded minors for k-level graphs. We also investigate the excluded minors for graphs of Theta rank 2. For the case of hypersimplices, that is, matroid base polytopes of uniform matroids, we present results about the non-negative rank and the Gröbner fan together with conjectures about possible generalizations to the class of Theta-1 matroids. v Acknowledgements The day I started my Ph.D., I was new to Discrete Geometry and to mathe- matical research. About a thousand days later, I’m at the end of this amazing academical journey and my biggest and most sincere thanks go to my ad- visor Raman Sanyal. For always trying to find time to discuss maths, even during the overwhelming weeks, for teaching me how to do research and how to present the results, for helping me out whenever I was stuck. I want to thank my coauthor Juanjo Rué for uncountably many (even for him) hours spent together at improving our paper and Arnau Padrol for the huge amount of time dedicated to answering my geometric questions. I’d like to express my gratitude towards Rekha Thomas, Francisco Santos, Bernd Sturmfels, and Federico Ardila for their challenging and helpful input. Besides useful discussions and several borrowed books, I’d like to thank Gün- ter Ziegler for directing the research group at the Villa. It has been a pleasure to work in a welcoming and challenging environment with my office mates To- bias Friedl, Katy Beeler, Albert Haase, and people from “upstairs” Katherina Jochemko, Nevena Palic, and Philip Brinkmann. I want to thank BMS and MDS for providing plenty of opportunities for young mathematicians, my mentor Tibor Szabó for his precious advise, Flo- rian Frick and Giulia Battiston for the enjoyable time spent together chat- ting about math and beyond, Bruno Benedetti for offering me the chance of tutoring a course, Lorenzo Prelli, Elena Lavanda, and David Rolnick for reading parts of my thesis. My last thanks go to my family and to Francesca for supporting me every single day from a distance. I’ve been abroad for three years and I haven’t felt alone one minute. vii Contents Summary v Acknowledgements vii Introduction xiii 1 Basics 1 1.1 Point configurations and polytopes . .1 1.1.1 Basic definitions and properties . .1 1.1.2 Gale duality . .7 1.2 Matroids . 11 1.2.1 Basic definitions and properties . 11 1.2.2 Matroid base configurations . 13 1.2.3 Matroid operations . 17 2 Configurations and levelness 19 2.1 Configurations of 0/1-points . 19 2.2 An overview of 2-level configurations . 21 2.2.1 Levelness and 2-levelness . 21 2.2.2 Polytopal constructions and levelness . 24 2.2.3 A catalog of 2-level polytopes . 24 2.3 Simple and simplicial . 27 3 Matroid base configurations 33 3.1 Face-hereditary properties . 33 3.2 2-level matroids . 35 3.3 k-level graphs . 40 3.3.1 Excluded minors for k-level graphs . 40 ix x CONTENTS 3.3.2 The class of 3-level graphs . 42 3.3.3 4-level and Theta-2 graphs . 44 3.4 Excluded minors for k-level matroids . 47 4 The constructive approach 55 4.1 Structural properties . 55 4.1.1 Tree decompositions of matroids . 55 4.1.2 The family of UMR-trees . 59 4.2 Base polytopes and 2-sum . 62 4.2.1 Sort-closed matroids and triangulations . 62 4.2.2 Alcoved polytopes and volumes . 67 5 Enumeration of 2-level matroids 71 5.1 The generating function T (x) .................. 71 5.1.1 Preliminaries . 71 5.1.2 The combinatorial class of UMR-trees . 72 5.1.3 Counting pointed UMR-trees . 73 5.1.4 The Dissymmetry Theorem . 76 5.2 Asymptotic analysis of T (x) ................... 79 5.2.1 Preliminaries . 79 5.2.2 Asymptotic analysis . 82 5.2.3 Self-duality . 86 5.2.4 Many 2-level polytopes from matroids . 89 6 Matroid ideals and cone-ranks 91 6.1 Degree of base configurations . 91 6.2 Psd rank . 94 6.3 Extension complexity of hypersimplices . 97 6.4 Vanishing ideal of uniform matroids . 101 6.4.1 Gröbner bases of In;k ................... 101 6.4.2 Gröbner fan and state polyhedron . 105 CONTENTS xi Bibliography 111 List of Figures 119 Zusammenfassung 121 Eidesstattliche Erklärung 123 Introduction Let V Rd be a configuration of finitely many points and c Rd a vector. ⊂ 2 If we are asked to maximize the linear function c; x = c1x1 + ::: + cdxd on V , we are dealing with a simple task, namely, toh evaluatei c; v for all v V and record the maximum value. h i 2 x2 x2 (1, 1) 1/2 ( 1 , 1) − 2 13 3 c, v 7/10 ( 5 , 5 ) h i − x1 = x1 ( 1, 0) (3, 0) ⇒ 1/2 3/2 − − ( 13 , 3 ) 19/10 5 − 5 − (1, 1) 3/2 − − Figure 1: Linear optimization over a configuration V . It is tempting to claim that linear optimization over a finite point configura- tion is computationally easy and, in particular, that the computational cost is linear in the number of points of V . These considerations rely on one key assumption, namely that V is provided as a finite list of points. For instance, a linear optimization over the configuration V leads to the same outcome if solved on the polytope P = conv(V ). x2 x +2x 1 1 2 ≥ − x1 x 2x 1 1− 2 ≥ − Figure 2: Linear inequalities defining conv(V ). xiii xiv CONTENTS Moreover, the polytope P = conv(V ) can be described by a system of linear inequalities Cx δ, where C Rm×d and δ Rm (see Figure 2). It is known that the linear≤ programming2 problem 2 max c; p s.t. Cp δ p2Rd h i ≤ can be solved in polynomial time [Sch03a, Ch. 5]. On the other hand, V could be described as the set of solutions to a system of non-linear polynomial equations (see Figure 3) in which case the direct approach to the optimization requires us to solve the system as a first step and therefore is rather unpractical. In general, performing linear optimiza- tion over a finite configuration of points defined by non-linear polynomial constraints is NP-hard [Lau09, Sect. 1]. x2+4x2 2x 3=0 1 2− 1− (x 2x +1)(x 2x 1.4)(x 2x 3)(x 2x 3.8)=0 1− 2 1− 2− 1− 2− 1− 2− (x1+2x2+1)(x1+2x2 1.4)(x1+2x2 3)(x1+2x2 3.8)=0 − − − x2 x1 Figure 3: A polynomial description of V . An alternative way of tackling the problem is to optimize over a relaxation of conv(V ), that is, a set containing conv(V ), and this yields an approximate so- lution in polynomial time. The key observation is that the polytope conv(V ) is determined by the set of all linear inequalities of the form `(x) 0, where `(x) is a non-negative linear function on V . ≥ A linear function `(x) = δ c; x which is non-negative on V is called k-sos −h i (sum of squares) with respect to V if there exist polynomials h ; : : : ; hs 1 2 R[x1; : : : ; xd] such that deg(hi) k and ≤ `(v) = h2(v) + h2(v) + + h2(v) (1) 1 2 ··· s for all v V . 2 CONTENTS xv Among the non-negative linear functions defining conv(V ) we consider the ones that are k-sos.
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