Polyhedral Combinatorics (ADM III)
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7 LATTICE POINTS and LATTICE POLYTOPES Alexander Barvinok
7 LATTICE POINTS AND LATTICE POLYTOPES Alexander Barvinok INTRODUCTION Lattice polytopes arise naturally in algebraic geometry, analysis, combinatorics, computer science, number theory, optimization, probability and representation the- ory. They possess a rich structure arising from the interaction of algebraic, convex, analytic, and combinatorial properties. In this chapter, we concentrate on the the- ory of lattice polytopes and only sketch their numerous applications. We briefly discuss their role in optimization and polyhedral combinatorics (Section 7.1). In Section 7.2 we discuss the decision problem, the problem of finding whether a given polytope contains a lattice point. In Section 7.3 we address the counting problem, the problem of counting all lattice points in a given polytope. The asymptotic problem (Section 7.4) explores the behavior of the number of lattice points in a varying polytope (for example, if a dilation is applied to the polytope). Finally, in Section 7.5 we discuss problems with quantifiers. These problems are natural generalizations of the decision and counting problems. Whenever appropriate we address algorithmic issues. For general references in the area of computational complexity/algorithms see [AB09]. We summarize the computational complexity status of our problems in Table 7.0.1. TABLE 7.0.1 Computational complexity of basic problems. PROBLEM NAME BOUNDED DIMENSION UNBOUNDED DIMENSION Decision problem polynomial NP-hard Counting problem polynomial #P-hard Asymptotic problem polynomial #P-hard∗ Problems with quantifiers unknown; polynomial for ∀∃ ∗∗ NP-hard ∗ in bounded codimension, reduces polynomially to volume computation ∗∗ with no quantifier alternation, polynomial time 7.1 INTEGRAL POLYTOPES IN POLYHEDRAL COMBINATORICS We describe some combinatorial and computational properties of integral polytopes. -
Polyhedral Combinatorics, Complexity & Algorithms
POLYHEDRAL COMBINATORICS, COMPLEXITY & ALGORITHMS FOR k-CLUBS IN GRAPHS By FOAD MAHDAVI PAJOUH Bachelor of Science in Industrial Engineering Sharif University of Technology Tehran, Iran 2004 Master of Science in Industrial Engineering Tarbiat Modares University Tehran, Iran 2006 Submitted to the Faculty of the Graduate College of Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY July, 2012 COPYRIGHT c By FOAD MAHDAVI PAJOUH July, 2012 POLYHEDRAL COMBINATORICS, COMPLEXITY & ALGORITHMS FOR k-CLUBS IN GRAPHS Dissertation Approved: Dr. Balabhaskar Balasundaram Dissertation Advisor Dr. Ricki Ingalls Dr. Manjunath Kamath Dr. Ramesh Sharda Dr. Sheryl Tucker Dean of the Graduate College iii Dedicated to my parents iv ACKNOWLEDGMENTS I would like to wholeheartedly express my sincere gratitude to my advisor, Dr. Balabhaskar Balasundaram, for his continuous support of my Ph.D. study, for his patience, enthusiasm, motivation, and valuable knowledge. It has been a great honor for me to be his first Ph.D. student and I could not have imagined having a better advisor for my Ph.D. research and role model for my future academic career. He has not only been a great mentor during the course of my Ph.D. and professional development but also a real friend and colleague. I hope that I can in turn pass on the research values and the dreams that he has given to me, to my future students. I wish to express my warm and sincere thanks to my wonderful committee mem- bers, Dr. Ricki Ingalls, Dr. Manjunath Kamath and Dr. Ramesh Sharda for their support, guidance and helpful suggestions throughout my Ph.D. -
Prizes and Awards Session
PRIZES AND AWARDS SESSION Wednesday, July 12, 2021 9:00 AM EDT 2021 SIAM Annual Meeting July 19 – 23, 2021 Held in Virtual Format 1 Table of Contents AWM-SIAM Sonia Kovalevsky Lecture ................................................................................................... 3 George B. Dantzig Prize ............................................................................................................................. 5 George Pólya Prize for Mathematical Exposition .................................................................................... 7 George Pólya Prize in Applied Combinatorics ......................................................................................... 8 I.E. Block Community Lecture .................................................................................................................. 9 John von Neumann Prize ......................................................................................................................... 11 Lagrange Prize in Continuous Optimization .......................................................................................... 13 Ralph E. Kleinman Prize .......................................................................................................................... 15 SIAM Prize for Distinguished Service to the Profession ....................................................................... 17 SIAM Student Paper Prizes .................................................................................................................... -
3. Linear Programming and Polyhedral Combinatorics Summary of What Was Seen in the Introductory Lectures on Linear Programming and Polyhedral Combinatorics
Massachusetts Institute of Technology Handout 6 18.433: Combinatorial Optimization February 20th, 2009 Michel X. Goemans 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory lectures on linear programming and polyhedral combinatorics. Definition 3.1 A halfspace in Rn is a set of the form fx 2 Rn : aT x ≤ bg for some vector a 2 Rn and b 2 R. Definition 3.2 A polyhedron is the intersection of finitely many halfspaces: P = fx 2 Rn : Ax ≤ bg. Definition 3.3 A polytope is a bounded polyhedron. n n−1 Definition 3.4 If P is a polyhedron in R , the projection Pk ⊆ R of P is defined as fy = (x1; x2; ··· ; xk−1; xk+1; ··· ; xn): x 2 P for some xkg. This is a special case of a projection onto a linear space (here, we consider only coordinate projection). By repeatedly projecting, we can eliminate any subset of coordinates. We claim that Pk is also a polyhedron and this can be proved by giving an explicit description of Pk in terms of linear inequalities. For this purpose, one uses Fourier-Motzkin elimination. Let P = fx : Ax ≤ bg and let • S+ = fi : aik > 0g, • S− = fi : aik < 0g, • S0 = fi : aik = 0g. T Clearly, any element in Pk must satisfy the inequality ai x ≤ bi for all i 2 S0 (these inequal- ities do not involve xk). Similarly, we can take a linear combination of an inequality in S+ and one in S− to eliminate the coefficient of xk. This shows that the inequalities: ! ! X X aik aljxj − alk aijxj ≤ aikbl − alkbi (1) j j for i 2 S+ and l 2 S− are satisfied by all elements of Pk. -
Linear Programming and Polyhedral Combinatorics Summary of What Was Seen in the Introductory Lectures on Linear Programming and Polyhedral Combinatorics
Massachusetts Institute of Technology Handout 8 18.433: Combinatorial Optimization March 6th, 2007 Michel X. Goemans Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory lectures on linear programming and polyhedral combinatorics. Definition 1 A halfspace in Rn is a set of the form fx 2 Rn : aT x ≤ bg for some vector a 2 Rn and b 2 R. Definition 2 A polyhedron is the intersection of finitely many halfspaces: P = fx 2 Rn : Ax ≤ bg. Definition 3 A polytope is a bounded polyhedron. n Definition 4 If P is a polyhedron in R , the projection Pk of P is defined as fy = (x1; x2; · · · ; xk−1; xk+1; · · · ; xn) : x 2 P for some xkg. We claim that Pk is also a polyhedron and this can be proved by giving an explicit description of Pk in terms of linear inequalities. For this purpose, one uses Fourier-Motzkin elimination. Let P = fx : Ax ≤ bg and let • S+ = fi : aik > 0g, • S− = fi : aik < 0g, • S0 = fi : aik = 0g. T Clearly, any element in Pk must satisfy the inequality ai x ≤ bi for all i 2 S0 (these inequal- ities do not involve xk). Similarly, we can take a linear combination of an inequality in S+ and one in S− to eliminate the coefficient of xk. This shows that the inequalities: aik aljxj − alk akjxj ≤ aikbl − alkbi (1) j ! j ! X X for i 2 S+ and l 2 S− are satisfied by all elements of Pk. Conversely, for any vector (x1; x2; · · · ; xk−1; xk+1; · · · ; xn) satisfying (1) for all i 2 S+ and l 2 S− and also T ai x ≤ bi for all i 2 S0 (2) we can find a value of xk such that the resulting x belongs to P (by looking at the bounds on xk that each constraint imposes, and showing that the largest lower bound is smaller than the smallest upper bound). -
ADDENDUM the Following Remarks Were Added in Proof (November 1966). Page 67. an Easy Modification of Exercise 4.8.25 Establishes
ADDENDUM The following remarks were added in proof (November 1966). Page 67. An easy modification of exercise 4.8.25 establishes the follow ing result of Wagner [I]: Every simplicial k-"complex" with at most 2 k ~ vertices has a representation in R + 1 such that all the "simplices" are geometric (rectilinear) simplices. Page 93. J. H. Conway (private communication) has established the validity of the conjecture mentioned in the second footnote. Page 126. For d = 2, the theorem of Derry [2] given in exercise 7.3.4 was found earlier by Bilinski [I]. Page 183. M. A. Perles (private communication) recently obtained an affirmative solution to Klee's problem mentioned at the end of section 10.1. Page 204. Regarding the question whether a(~) = 3 implies b(~) ~ 4, it should be noted that if one starts from a topological cell complex ~ with a(~) = 3 it is possible that ~ is not a complex (in our sense) at all (see exercise 11.1.7). On the other hand, G. Wegner pointed out (in a private communication to the author) that the 2-complex ~ discussed in the proof of theorem 11.1 .7 indeed satisfies b(~) = 4. Page 216. Halin's [1] result (theorem 11.3.3) has recently been genera lized by H. A. lung to all complete d-partite graphs. (Halin's result deals with the graph of the d-octahedron, i.e. the d-partite graph in which each class of nodes contains precisely two nodes.) The existence of the numbers n(k) follows from a recent result of Mader [1] ; Mader's result shows that n(k) ~ k.2(~) . -
Combinatorial Optimization, Packing and Covering
Combinatorial Optimization: Packing and Covering G´erard Cornu´ejols Carnegie Mellon University July 2000 1 Preface The integer programming models known as set packing and set cov- ering have a wide range of applications, such as pattern recognition, plant location and airline crew scheduling. Sometimes, due to the spe- cial structure of the constraint matrix, the natural linear programming relaxation yields an optimal solution that is integer, thus solving the problem. Sometimes, both the linear programming relaxation and its dual have integer optimal solutions. Under which conditions do such integrality properties hold? This question is of both theoretical and practical interest. Min-max theorems, polyhedral combinatorics and graph theory all come together in this rich area of discrete mathemat- ics. In addition to min-max and polyhedral results, some of the deepest results in this area come in two flavors: “excluded minor” results and “decomposition” results. In these notes, we present several of these beautiful results. Three chapters cover min-max and polyhedral re- sults. The next four cover excluded minor results. In the last three, we present decomposition results. We hope that these notes will encourage research on the many intriguing open questions that still remain. In particular, we state 18 conjectures. For each of these conjectures, we offer $5000 as an incentive for the first correct solution or refutation before December 2020. 2 Contents 1Clutters 7 1.1 MFMC Property and Idealness . 9 1.2 Blocker . 13 1.3 Examples .......................... 15 1.3.1 st-Cuts and st-Paths . 15 1.3.2 Two-Commodity Flows . 17 1.3.3 r-Cuts and r-Arborescences . -
Polyhedral Combinatorics : an Annotated Bibliography
Polyhedral combinatorics : an annotated bibliography Citation for published version (APA): Aardal, K. I., & Weismantel, R. (1996). Polyhedral combinatorics : an annotated bibliography. (Universiteit Utrecht. UU-CS, Department of Computer Science; Vol. 9627). Utrecht University. Document status and date: Published: 01/01/1996 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. -
The Traveling Preacher Problem, Report No
Discrete Optimization 5 (2008) 290–292 www.elsevier.com/locate/disopt The travelling preacher, projection, and a lower bound for the stability number of a graph$ Kathie Camerona,∗, Jack Edmondsb a Wilfrid Laurier University, Waterloo, Ontario, Canada N2L 3C5 b EP Institute, Kitchener, Ontario, Canada N2M 2M6 Received 9 November 2005; accepted 27 August 2007 Available online 29 October 2007 In loving memory of George Dantzig Abstract The coflow min–max equality is given a travelling preacher interpretation, and is applied to give a lower bound on the maximum size of a set of vertices, no two of which are joined by an edge. c 2007 Elsevier B.V. All rights reserved. Keywords: Network flow; Circulation; Projection of a polyhedron; Gallai’s conjecture; Stable set; Total dual integrality; Travelling salesman cost allocation game 1. Coflow and the travelling preacher An interpretation and an application prompt us to recall, and hopefully promote, an older combinatorial min–max equality called the Coflow Theorem. Let G be a digraph. For each edge e of G, let de be a non-negative integer. The capacity d(C) of a dicircuit C means the sum of the de’s of the edges in C. An instance of the Coflow Theorem (1982) [2,3] says: Theorem 1. The maximum cardinality of a subset S of the vertices of G such that each dicircuit C of G contains at most d(C) members of S equals the minimum of the sum of the capacities of any subset H of dicircuits of G plus the number of vertices of G which are not in a dicircuit of H. -
36 COMPUTATIONAL CONVEXITY Peter Gritzmann and Victor Klee
36 COMPUTATIONAL CONVEXITY Peter Gritzmann and Victor Klee INTRODUCTION The subject of Computational Convexity draws its methods from discrete mathe- matics and convex geometry, and many of its problems from operations research, computer science, data analysis, physics, material science, and other applied ar- eas. In essence, it is the study of the computational and algorithmic aspects of high-dimensional convex sets (especially polytopes), with a view to applying the knowledge gained to convex bodies that arise in other mathematical disciplines or in the mathematical modeling of problems from outside mathematics. The name Computational Convexity is of more recent origin, having first ap- peared in print in 1989. However, results that retrospectively belong to this area go back a long way. In particular, many of the basic ideas of Linear Programming have an essentially geometric character and fit very well into the conception of Compu- tational Convexity. The same is true of the subject of Polyhedral Combinatorics and of the Algorithmic Theory of Polytopes and Convex Bodies. The emphasis in Computational Convexity is on problems whose underlying structure is the convex geometry of normed vector spaces of finite but generally not restricted dimension, rather than of fixed dimension. This leads to closer con- nections with the optimization problems that arise in a wide variety of disciplines. Further, in the study of Computational Convexity, the underlying model of com- putation is mainly the binary (Turing machine) model that is common in studies of computational complexity. This requirement is imposed by prospective appli- cations, particularly in mathematical programming. For the study of algorithmic aspects of convex bodies that are not polytopes, the binary model is often aug- mented by additional devices called \oracles." Some cases of interest involve other models of computation, but the present discussion focuses on aspects of computa- tional convexity for which binary models seem most natural. -
Introduction to Polyhedral Combinatorics
Introduction to Polyhedral Combinatorics Based on the lectures of András Frank Written by: Attila Joó University of Hamburg Mathematics Department Last modification: March 26, 2019 Contents 1 Introduction to linear programming .................................. 1 Basic notation ................................................. 1 Fourier-Motzkin elimination.......................................... 1 Polyhedral cones and generated cones .................................... 1 Bounds for a linear program.......................................... 2 Basic solutions ................................................. 3 Strong duality ................................................. 4 The structure of polyhedrons ......................................... 5 2 TU matrices ................................................. 6 Examples .................................................... 6 Properties of TU matrices........................................... 7 Applications of TU matrices.......................................... 8 Applications in bipartite graphs..................................... 8 Applications in digraphs......................................... 10 A further application........................................... 12 3 Total dual integrality ............................................ 13 Submodular flows................................................ 14 Applications of subflows............................................ 15 i 1 Introduction to linear programming Basic notation n Pn The scalar product of vectors x; y 2 R is xy := i=1 -
S-Hypersimplices, Pulling Triangulations, and Monotone Paths
S-HYPERSIMPLICES, PULLING TRIANGULATIONS, AND MONOTONE PATHS SEBASTIAN MANECKE, RAMAN SANYAL, AND JEONGHOON SO Abstract. An S-hypersimplex for S ⊆ f0; 1; : : : ; dg is the convex hull of all 0=1-vectors of length d with coordinate sum in S. These polytopes generalize the classical hypersimplices as well as cubes, crosspoly- topes, and halfcubes. In this paper we study faces and dissections of S-hypersimplices. Moreover, we show that monotone path polytopes of S-hypersimplices yield all types of multipermutahedra. In analogy to cubes, we also show that the number of simplices in a pulling triangulation of a halfcube is independent of the pulling order. 1. Introduction d The cube d = [0; 1] together with the simplex ∆d = conv(0; e1;:::; ed) and the cross-polytope ♦d = conv(±e1;:::; ±ed) constitute the Big Three, three infinite families of convex polytopes whose geometric and combinatorial features make them ubiquitous throughout mathematics. A close cousin to the cube is the (even) halfcube d Hd := conv p 2 f0; 1g : p1 + ··· + pd even : d The halfcubes H1 and H2 are a point and a segment, respectively, but for d ≥ 3, Hd ⊂ R is a full- dimensional polytope. The 5-dimensional halfcube was already described by Thomas Gosset [11] in his classification of semi-regular polytopes. In contemporary mathematics, halfcubes appear under the name of demi(hyper)cubes [7] or parity polytopes [26]. In particular the name ‘parity polytope’ suggests a connection to combinatorial optimization and polyhedral combinatorics; see [6, 10] for more. However, halfcubes also occur in algebraic/topological combinatorics [13, 14], convex algebraic geometry [22], and in many more areas.