Algebraic Matroids and Frobenius Flocks
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Hermite–Lindemann–Weierstraß Transcendence Theorem
The Hermite–Lindemann–Weierstraß Transcendence Theorem Manuel Eberl March 12, 2021 Abstract This article provides a formalisation of the Hermite–Lindemann– Weierstraß Theorem (also known as simply Hermite–Lindemann or Lindemann–Weierstraß). This theorem is one of the crowning achieve- ments of 19th century number theory. The theorem states that if α1; : : : ; αn 2 C are algebraic numbers that are linearly independent over Z, then eα1 ; : : : ; eαn are algebraically independent over Q. Like the previous formalisation in Coq by Bernard [2], I proceeded by formalising Baker’s alternative formulation of the theorem [1] and then deriving the original one from that. Baker’s version states that for any algebraic numbers β1; : : : ; βn 2 C and distinct algebraic numbers αi; : : : ; αn 2 C, we have: α1 αn β1e + ::: + βne = 0 iff 8i: βi = 0 This has a number of immediate corollaries, e.g.: • e and π are transcendental • ez, sin z, tan z, etc. are transcendental for algebraic z 2 C n f0g • ln z is transcendental for algebraic z 2 C n f0; 1g 1 Contents 1 Divisibility of algebraic integers 3 2 Auxiliary facts about univariate polynomials 6 3 The minimal polynomial of an algebraic number 10 4 The lexicographic ordering on complex numbers 12 5 Additional facts about multivariate polynomials 13 5.1 Miscellaneous ........................... 13 5.2 Converting a univariate polynomial into a multivariate one . 14 6 More facts about algebraic numbers 15 6.1 Miscellaneous ........................... 15 6.2 Turning an algebraic number into an algebraic integer .... 18 6.3 Multiplying an algebraic number with a suitable integer turns it into an algebraic integer. -
Some Topics Concerning Graphs, Signed Graphs and Matroids
SOME TOPICS CONCERNING GRAPHS, SIGNED GRAPHS AND MATROIDS DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University By Vaidyanathan Sivaraman, M.S. Graduate Program in Mathematics The Ohio State University 2012 Dissertation Committee: Prof. Neil Robertson, Advisor Prof. Akos´ Seress Prof. Matthew Kahle ABSTRACT We discuss well-quasi-ordering in graphs and signed graphs, giving two short proofs of the bounded case of S. B. Rao's conjecture. We give a characterization of graphs whose bicircular matroids are signed-graphic, thus generalizing a theorem of Matthews from the 1970s. We prove a recent conjecture of Zaslavsky on the equality of frus- tration number and frustration index in a certain class of signed graphs. We prove that there are exactly seven signed Heawood graphs, up to switching isomorphism. We present a computational approach to an interesting conjecture of D. J. A. Welsh on the number of bases of matroids. We then move on to study the frame matroids of signed graphs, giving explicit signed-graphic representations of certain families of matroids. We also discuss the cycle, bicircular and even-cycle matroid of a graph and characterize matroids arising as two different such structures. We study graphs in which any two vertices have the same number of common neighbors, giving a quick proof of Shrikhande's theorem. We provide a solution to a problem of E. W. Dijkstra. Also, we discuss the flexibility of graphs on the projective plane. We conclude by men- tioning partial progress towards characterizing signed graphs whose frame matroids are transversal, and some miscellaneous results. -
Quasipolynomial Representation of Transversal Matroids with Applications in Parameterized Complexity
Quasipolynomial Representation of Transversal Matroids with Applications in Parameterized Complexity Daniel Lokshtanov1, Pranabendu Misra2, Fahad Panolan1, Saket Saurabh1,2, and Meirav Zehavi3 1 University of Bergen, Bergen, Norway. {daniello,pranabendu.misra,fahad.panolan}@ii.uib.no 2 The Institute of Mathematical Sciences, HBNI, Chennai, India. [email protected] 3 Ben-Gurion University, Beersheba, Israel. [email protected] Abstract Deterministic polynomial-time computation of a representation of a transversal matroid is a longstanding open problem. We present a deterministic computation of a so-called union rep- resentation of a transversal matroid in time quasipolynomial in the rank of the matroid. More precisely, we output a collection of linear matroids such that a set is independent in the trans- versal matroid if and only if it is independent in at least one of them. Our proof directly implies that if one is interested in preserving independent sets of size at most r, for a given r ∈ N, but does not care whether larger independent sets are preserved, then a union representation can be computed deterministically in time quasipolynomial in r. This consequence is of independent interest, and sheds light on the power of union representation. Our main result also has applications in Parameterized Complexity. First, it yields a fast computation of representative sets, and due to our relaxation in the context of r, this computation also extends to (standard) truncations. In turn, this computation enables to efficiently solve various problems, such as subcases of subgraph isomorphism, motif search and packing problems, in the presence of color lists. Such problems have been studied to model scenarios where pairs of elements to be matched may not be identical but only similar, and color lists aim to describe the set of compatible elements associated with each element. -
Partial Fields and Matroid Representation
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by UC Research Repository Partial Fields and Matroid Representation Charles Semple and Geoff Whittle Department of Mathematics Victoria University PO Box 600 Wellington New Zealand April 10, 1995 Abstract A partial field P is an algebraic structure that behaves very much like a field except that addition is a partial binary operation, that is, for some a; b ∈ P, a + b may not be defined. We develop a theory of matroid representation over partial fields. It is shown that many im- portant classes of matroids arise as the class of matroids representable over a partial field. The matroids representable over a partial field are closed under standard matroid operations such as the taking of mi- nors, duals, direct sums and 2{sums. Homomorphisms of partial fields are defined. It is shown that if ' : P1 → P2 is a non-trivial partial field homomorphism, then every matroid representable over P1 is rep- resentable over P2. The connection with Dowling group geometries is examined. It is shown that if G is a finite abelian group, and r>2, then there exists a partial field over which the rank{r Dowling group geometry is representable if and only if G has at most one element of order 2, that is, if G is a group in which the identity has at most two square roots. 1 1 Introduction It follows from a classical (1958) result of Tutte [19] that a matroid is rep- resentable over GF (2) and some field of characteristic other than 2 if and only if it can be represented over the rationals by the columns of a totally unimodular matrix, that is, by a matrix over the rationals all of whose non- zero subdeterminants are in {1; −1}. -
Parameterized Algorithms Using Matroids Lecture I: Matroid Basics and Its Use As Data Structure
Parameterized Algorithms using Matroids Lecture I: Matroid Basics and its use as data structure Saket Saurabh The Institute of Mathematical Sciences, India and University of Bergen, Norway, ADFOCS 2013, MPI, August 5{9, 2013 1 Introduction and Kernelization 2 Fixed Parameter Tractable (FPT) Algorithms For decision problems with input size n, and a parameter k, (which typically is the solution size), the goal here is to design an algorithm with (1) running time f (k) nO , where f is a function of k alone. · Problems that have such an algorithm are said to be fixed parameter tractable (FPT). 3 A Few Examples Vertex Cover Input: A graph G = (V ; E) and a positive integer k. Parameter: k Question: Does there exist a subset V 0 V of size at most k such ⊆ that for every edge( u; v) E either u V 0 or v V 0? 2 2 2 Path Input: A graph G = (V ; E) and a positive integer k. Parameter: k Question: Does there exist a path P in G of length at least k? 4 Kernelization: A Method for Everyone Informally: A kernelization algorithm is a polynomial-time transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size and parameter bounded by a function of the parameter. 5 Kernel: Formally Formally: A kernelization algorithm, or in short, a kernel for a parameterized problem L Σ∗ N is an algorithm that given ⊆ × (x; k) Σ∗ N, outputs in p( x + k) time a pair( x 0; k0) Σ∗ N such that 2 × j j 2 × • (x; k) L (x 0; k0) L , 2 () 2 • x 0 ; k0 f (k), j j ≤ where f is an arbitrary computable function, and p a polynomial. -
Commutative Algebra
Commutative Algebra Andrew Kobin Spring 2016 / 2019 Contents Contents Contents 1 Preliminaries 1 1.1 Radicals . .1 1.2 Nakayama's Lemma and Consequences . .4 1.3 Localization . .5 1.4 Transcendence Degree . 10 2 Integral Dependence 14 2.1 Integral Extensions of Rings . 14 2.2 Integrality and Field Extensions . 18 2.3 Integrality, Ideals and Localization . 21 2.4 Normalization . 28 2.5 Valuation Rings . 32 2.6 Dimension and Transcendence Degree . 33 3 Noetherian and Artinian Rings 37 3.1 Ascending and Descending Chains . 37 3.2 Composition Series . 40 3.3 Noetherian Rings . 42 3.4 Primary Decomposition . 46 3.5 Artinian Rings . 53 3.6 Associated Primes . 56 4 Discrete Valuations and Dedekind Domains 60 4.1 Discrete Valuation Rings . 60 4.2 Dedekind Domains . 64 4.3 Fractional and Invertible Ideals . 65 4.4 The Class Group . 70 4.5 Dedekind Domains in Extensions . 72 5 Completion and Filtration 76 5.1 Topological Abelian Groups and Completion . 76 5.2 Inverse Limits . 78 5.3 Topological Rings and Module Filtrations . 82 5.4 Graded Rings and Modules . 84 6 Dimension Theory 89 6.1 Hilbert Functions . 89 6.2 Local Noetherian Rings . 94 6.3 Complete Local Rings . 98 7 Singularities 106 7.1 Derived Functors . 106 7.2 Regular Sequences and the Koszul Complex . 109 7.3 Projective Dimension . 114 i Contents Contents 7.4 Depth and Cohen-Macauley Rings . 118 7.5 Gorenstein Rings . 127 8 Algebraic Geometry 133 8.1 Affine Algebraic Varieties . 133 8.2 Morphisms of Affine Varieties . 142 8.3 Sheaves of Functions . -
Matroid Theory Release 9.4
Sage 9.4 Reference Manual: Matroid Theory Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Basics 1 2 Built-in families and individual matroids 77 3 Concrete implementations 97 4 Abstract matroid classes 149 5 Advanced functionality 161 6 Internals 173 7 Indices and Tables 197 Python Module Index 199 Index 201 i ii CHAPTER ONE BASICS 1.1 Matroid construction 1.1.1 Theory Matroids are combinatorial structures that capture the abstract properties of (linear/algebraic/...) dependence. For- mally, a matroid is a pair M = (E; I) of a finite set E, the groundset, and a collection of subsets I, the independent sets, subject to the following axioms: • I contains the empty set • If X is a set in I, then each subset of X is in I • If two subsets X, Y are in I, and jXj > jY j, then there exists x 2 X − Y such that Y + fxg is in I. See the Wikipedia article on matroids for more theory and examples. Matroids can be obtained from many types of mathematical structures, and Sage supports a number of them. There are two main entry points to Sage’s matroid functionality. The object matroids. contains a number of con- structors for well-known matroids. The function Matroid() allows you to define your own matroids from a variety of sources. We briefly introduce both below; follow the links for more comprehensive documentation. Each matroid object in Sage comes with a number of built-in operations. An overview can be found in the documen- tation of the abstract matroid class. -
Lecture 13 — February, 1 2014 1 Overview 2 Matroids
Advanced Graph Algorithms Jan-Apr 2014 Lecture 13 | February, 1 2014 Lecturer: Saket Saurabh Scribe: Sanjukta Roy 1 Overview In this lecture we learn what is Matroid, the connection between greedy algorithms and matroids. We also look at some examples of Matroids e.g., Linear Matroids, Graphic Matroids etc. 2 Matroids 2.1 A Greedy Approach Let G = (V, E) be a connected undirected graph and let w : E ! R≥0 be a weight function on the edges. For MWST Kruskal's so-called greedy algorithm works. Consider Maximum Weight Matching problem. 1 a b 3 3 d c 4 Application of the greedy algorithm gives (d,c) and (a,b). However, (d,c) and (a,b) do not form a matching of maximum weight. It is obviously not true that such a greedy approach would lead to an optimal solution for any combinatorial optimization problem. It turns out that the structures for which the greedy algorithm does lead to an optimal solution, are the matroids. 2.2 Matroids Definition 1. A pair M = (E, I), where E is a ground set and I is a family of subsets (called independent sets) of E, is a matroid if it satisfies the following conditions: (I1) φ 2 IorI = ø: 1 (I2) If A0 ⊆ A and A 2 I then A0 2 I. (I3) If A, B 2 I and jAj < jBj, then 9e 2 (B n A) such that A [feg 2 I: The axiom (I2) is also called the hereditary property and a pair M = (E, I) satisfying (I1) and (I2) is called hereditary family or set-family. -
Partial Fields and Matroid Representation
ADVANCES IN APPLIED MATHEMATICS 17, 184]208Ž. 1996 ARTICLE NO. 0010 Partial Fields and Matroid Representation Charles Semple and Geoff Whittle Department of Mathematics, Victoria Uni¨ersity, PO Box 600 Wellington, New Zealand Received May 12, 1995 A partial field P is an algebraic structure that behaves very much like a field except that addition is a partial binary operation, that is, for some a, b g P, a q b may not be defined. We develop a theory of matroid representation over partial fields. It is shown that many important classes of matroids arise as the class of matroids representable over a partial field. The matroids representable over a partial field are closed under standard matroid operations such as the taking of minors, duals, direct sums, and 2-sums. Homomorphisms of partial fields are defined. It is shown that if w: P12ª P is a non-trivial partial-field homomorphism, then every matroid representable over P12is representable over P . The connec- tion with Dowling group geometries is examined. It is shown that if G is a finite abelian group, and r ) 2, then there exists a partial field over which the rank-r Dowling group geometry is representable if and only if G has at most one element of order 2, that is, if G is a group in which the identity has at most two square roots. Q 1996 Academic Press, Inc. 1. INTRODUCTION It follows from a classical result of Tuttewx 19 that a matroid is repre- sentable over GFŽ.2 and some field of characteristic other than 2 if and only if it can be represented over the rationals by the columns of a totally unimodular matrix, that is, by a matrix over the rationals all of whose non-zero subdeterminants are inÄ4 1, y1 . -
Algebraic Matroids in Applications by Zvi H Rosen a Dissertation
Algebraic Matroids in Applications by Zvi H Rosen A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Bernd Sturmfels, Chair Professor Lior Pachter Professor Yun S. Song Spring 2015 Algebraic Matroids in Applications Copyright 2015 by Zvi H Rosen 1 Abstract Algebraic Matroids in Applications by Zvi H Rosen Doctor of Philosophy in Mathematics University of California, Berkeley Professor Bernd Sturmfels, Chair Algebraic matroids are combinatorial objects defined by the set of coordinates of an algebraic variety. These objects are of interest whenever coordinates hold significance: for instance, when the variety describes solution sets for a real world problem, or is defined using some combinatorial rule. In this thesis, we discuss algebraic matroids, and explore tools for their computation. We then delve into two applications that involve algebraic matroids: probability matrices and tensors from statistics, and chemical reaction networks from biology. i In memory of my grandparents יצחק אהרN וחנה רייזל דייוויס ז"ל! Isaac and Ann Davis יצחק יוסP ושרה רוזN ז"ל! Isaac and Sala Rosen whose courage and perseverance through adversity will inspire their families for generations. ii Contents Contents ii List of Figures iv List of Tables v 1 Introduction 1 1.1 Summary of Main Results . 1 1.2 Examples . 2 1.3 Definitions, Axioms, and Notation . 5 2 Computation 10 2.1 Symbolic Algorithm . 11 2.2 Linear Algebra . 11 2.3 Sample Computations for Applications . 15 3 Statistics: Joint Probability Matroid 25 3.1 Completability of Partial Probability Matrix . -
210 the Final Chapter, on Algebraic Independence of Transcendental
210 BOOK REVIEWS [July The final chapter, on algebraic independence of transcendental numbers, contains an exposition of Siegel's method, by means of which it can be shown, under suitable circumstances, that alge braically independent solutions of a system of linear differential equa tions have algebraically independent values for algebraic argument. The general framework of the method is set out very clearly, and applications are made to (a) Lindemann's theorem, that if cei, • • • , an are algebraic numbers, linearly independent over the rationals, then e<*it . 9 ectn are algebraically independent over the rationals; and (b) Siegel's theorem, that if XQT^O is algebraic, the values JQ(XQ) and Jo(x0) of the Bessel function are algebraically independent over the rationals. The book ends with an interesting list of open problems which the author considers both important and attainable. W. J. LEVEQUE Applied analysis. By Cornelius Lanczos. Englewood Cliffs, Prentice Hall, 1956. 20+539 pp. $9.00. This is a systematic development of some of the ideas of Professor Lanczos in the field of applied analysis, rather than another textbook. Instead of the adjective "applied," the author suggests that "parexic"1 would be more appropriate. Parexic analysis lies between classical analysis and numerical analysis: it is roughly the theory of approxi mation by finite (or truncated infinite) algorithms. There is, therefore, some overlap with what is being called elsewhere "constructive theory of functions." However, this book is a very personal one, and has been influenced by the varied experiences of the author: in regular aca demic work, in government service, in industry, and now at the Dublin Institute for Advanced Study. -
Algebraic Independence
Algebraic Independence UGP : CS498A Report Advisor : Dr. Nitin Saxena Tushant Mittal Indian Institute of Technology, Kanpur Contents 1 Introduction 2 1.1 The Problem........................................2 1.2 Motivation.........................................2 1.3 Preliminary Definitions..................................3 2 Previous Work 4 2.1 Computability.......................................4 2.1.1 The \Brute force" Algorithm...........................4 2.2 Characteristic 0 (or large) fields.............................5 2.3 Witt-Jacobian Criterion..................................5 2.4 Generalizing the Jacobian.................................5 3 Dimension Reduction6 3.1 Notation..........................................6 3.2 The first approach....................................6 3.3 The k-gap.........................................7 3.3.1 Bivariate Case...................................7 4 New Criterion 9 4.1 Ideal Shrink........................................9 4.2 Criterion.......................................... 10 5 Conclusion and Future Directions 12 6 Acknowledgements 13 1 Chapter 1 Introduction 1.1 The Problem The concept of algebraic independence is a natural generalization of the familiar notion of linear dependence. More formally, Definition 1.1. A subset S of a field L is algebraically dependent over a subfield K if the elements of S satisfy a non-trivial polynomial equation with coefficients in K. ♦ A few concrete examples are : • Algebraic/Transcendental Numbers : L = C ;K = Q;S = fαg • Polynomials : L = F(x1; ··· ; xn);K = F;S = ff1; ··· ; fng The problem of testing algebraic independence is then, Given a set of polynomials ff1; ··· ; fng determine if they are algebraically dependent i.e does there 9 A 2 F[y1; ··· ; yn] such that A(f1; ··· ; fn) = 0. ( A is called its annihilating polynomial ). Examples 1. The set f = fx1; x2; ··· ; xkg is always algebraically independent. p p 2. Algebraic dependence depends on the underlying field, fx1 +x2; x1 +x2g is independent over p Q but is dependent over Fp with y2 − y1 as the annihilating polynomial.