Some Applications of Algebra to Combinatorics
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LINEAR ALGEBRA METHODS in COMBINATORICS László Babai
LINEAR ALGEBRA METHODS IN COMBINATORICS L´aszl´oBabai and P´eterFrankl Version 2.1∗ March 2020 ||||| ∗ Slight update of Version 2, 1992. ||||||||||||||||||||||| 1 c L´aszl´oBabai and P´eterFrankl. 1988, 1992, 2020. Preface Due perhaps to a recognition of the wide applicability of their elementary concepts and techniques, both combinatorics and linear algebra have gained increased representation in college mathematics curricula in recent decades. The combinatorial nature of the determinant expansion (and the related difficulty in teaching it) may hint at the plausibility of some link between the two areas. A more profound connection, the use of determinants in combinatorial enumeration goes back at least to the work of Kirchhoff in the middle of the 19th century on counting spanning trees in an electrical network. It is much less known, however, that quite apart from the theory of determinants, the elements of the theory of linear spaces has found striking applications to the theory of families of finite sets. With a mere knowledge of the concept of linear independence, unexpected connections can be made between algebra and combinatorics, thus greatly enhancing the impact of each subject on the student's perception of beauty and sense of coherence in mathematics. If these adjectives seem inflated, the reader is kindly invited to open the first chapter of the book, read the first page to the point where the first result is stated (\No more than 32 clubs can be formed in Oddtown"), and try to prove it before reading on. (The effect would, of course, be magnified if the title of this volume did not give away where to look for clues.) What we have said so far may suggest that the best place to present this material is a mathematics enhancement program for motivated high school students. -
Schaum's Outline of Linear Algebra (4Th Edition)
SCHAUM’S SCHAUM’S outlines outlines Linear Algebra Fourth Edition Seymour Lipschutz, Ph.D. Temple University Marc Lars Lipson, Ph.D. University of Virginia Schaum’s Outline Series New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2009, 2001, 1991, 1968 by The McGraw-Hill Companies, Inc. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior writ- ten permission of the publisher. ISBN: 978-0-07-154353-8 MHID: 0-07-154353-8 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-154352-1, MHID: 0-07-154352-X. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. To contact a representative please e-mail us at [email protected]. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licensors reserve all rights in and to the work. -
A Combinatorial Analysis of Finite Boolean Algebras
A Combinatorial Analysis of Finite Boolean Algebras Kevin Halasz [email protected] May 1, 2013 Copyright c Kevin Halasz. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license can be found at http://www.gnu.org/copyleft/fdl.html. 1 Contents 1 Introduction 3 2 Basic Concepts 3 2.1 Chains . .3 2.2 Antichains . .6 3 Dilworth's Chain Decomposition Theorem 6 4 Boolean Algebras 8 5 Sperner's Theorem 9 5.1 The Sperner Property . .9 5.2 Sperner's Theorem . 10 6 Extensions 12 6.1 Maximally Sized Antichains . 12 6.2 The Erdos-Ko-Rado Theorem . 13 7 Conclusion 14 2 1 Introduction Boolean algebras serve an important purpose in the study of algebraic systems, providing algebraic structure to the notions of order, inequality, and inclusion. The algebraist is always trying to understand some structured set using symbol manipulation. Boolean algebras are then used to study the relationships that hold between such algebraic structures while still using basic techniques of symbol manipulation. In this paper we will take a step back from the standard algebraic practices, and analyze these fascinating algebraic structures from a different point of view. Using combinatorial tools, we will provide an in-depth analysis of the structure of finite Boolean algebras. We will start by introducing several ways of analyzing poset substructure from a com- binatorial point of view. -
The Number of Graded Partially Ordered Sets
JOURNAL OF COMBINATORIAL THEORY 6, 12-19 (1969) The Number of Graded Partially Ordered Sets DAVID A. KLARNER* McMaster University, Hamilton, Ontario, Canada Communicated by N. G. de Bruijn ABSTRACT We find an explicit formula for the number of graded partially ordered sets of rank h that can be defined on a set containing n elements. Also, we find the number of graded partially ordered sets of length h, and having a greatest and least element that can be defined on a set containing n elements. The first result provides a lower bound for G*(n), the number of posets that can be defined on an n-set; the second result provides an upper bound for the number of lattices satisfying the Jordan-Dedekind chain condition that can be defined on an n-set. INTRODUCTION The terminology we will use in connection with partially ordered sets is defined in detail in the first few pages of Birkhoff [1]; however, we need a few concepts not defined there. A rank function g maps the elements of a poset P into the chain of integers such that (i) x > y implies g(x) > g(y), and (ii) g(x) = g(y) + 1 if x covers y. A poset P is graded if at least one rank function can be defined on it; Birkhoff calls the pair (P, g) a graded poset, where g is a particular rank function defined on a poset P, so our definition differs from his. A path in a poset P is a sequence (xl ,..., x~) such that x~ covers or is covered by xi+l, for i = 1 ... -
Problems in Abstract Algebra
STUDENT MATHEMATICAL LIBRARY Volume 82 Problems in Abstract Algebra A. R. Wadsworth 10.1090/stml/082 STUDENT MATHEMATICAL LIBRARY Volume 82 Problems in Abstract Algebra A. R. Wadsworth American Mathematical Society Providence, Rhode Island Editorial Board Satyan L. Devadoss John Stillwell (Chair) Erica Flapan Serge Tabachnikov 2010 Mathematics Subject Classification. Primary 00A07, 12-01, 13-01, 15-01, 20-01. For additional information and updates on this book, visit www.ams.org/bookpages/stml-82 Library of Congress Cataloging-in-Publication Data Names: Wadsworth, Adrian R., 1947– Title: Problems in abstract algebra / A. R. Wadsworth. Description: Providence, Rhode Island: American Mathematical Society, [2017] | Series: Student mathematical library; volume 82 | Includes bibliographical references and index. Identifiers: LCCN 2016057500 | ISBN 9781470435837 (alk. paper) Subjects: LCSH: Algebra, Abstract – Textbooks. | AMS: General – General and miscellaneous specific topics – Problem books. msc | Field theory and polyno- mials – Instructional exposition (textbooks, tutorial papers, etc.). msc | Com- mutative algebra – Instructional exposition (textbooks, tutorial papers, etc.). msc | Linear and multilinear algebra; matrix theory – Instructional exposition (textbooks, tutorial papers, etc.). msc | Group theory and generalizations – Instructional exposition (textbooks, tutorial papers, etc.). msc Classification: LCC QA162 .W33 2017 | DDC 512/.02–dc23 LC record available at https://lccn.loc.gov/2016057500 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. -
From Arithmetic to Algebra
From arithmetic to algebra Slightly edited version of a presentation at the University of Oregon, Eugene, OR February 20, 2009 H. Wu Why can’t our students achieve introductory algebra? This presentation specifically addresses only introductory alge- bra, which refers roughly to what is called Algebra I in the usual curriculum. Its main focus is on all students’ access to the truly basic part of algebra that an average citizen needs in the high- tech age. The content of the traditional Algebra II course is on the whole more technical and is designed for future STEM students. In place of Algebra II, future non-STEM would benefit more from a mathematics-culture course devoted, for example, to an understanding of probability and data, recently solved famous problems in mathematics, and history of mathematics. At least three reasons for students’ failure: (A) Arithmetic is about computation of specific numbers. Algebra is about what is true in general for all numbers, all whole numbers, all integers, etc. Going from the specific to the general is a giant conceptual leap. Students are not prepared by our curriculum for this leap. (B) They don’t get the foundational skills needed for algebra. (C) They are taught incorrect mathematics in algebra classes. Garbage in, garbage out. These are not independent statements. They are inter-related. Consider (A) and (B): The K–3 school math curriculum is mainly exploratory, and will be ignored in this presentation for simplicity. Grades 5–7 directly prepare students for algebra. Will focus on these grades. Here, abstract mathematics appears in the form of fractions, geometry, and especially negative fractions. -
Arxiv:1508.05446V2 [Math.CO] 27 Sep 2018 02,5B5 16E10
CELL COMPLEXES, POSET TOPOLOGY AND THE REPRESENTATION THEORY OF ALGEBRAS ARISING IN ALGEBRAIC COMBINATORICS AND DISCRETE GEOMETRY STUART MARGOLIS, FRANCO SALIOLA, AND BENJAMIN STEINBERG Abstract. In recent years it has been noted that a number of combi- natorial structures such as real and complex hyperplane arrangements, interval greedoids, matroids and oriented matroids have the structure of a finite monoid called a left regular band. Random walks on the monoid model a number of interesting Markov chains such as the Tsetlin library and riffle shuffle. The representation theory of left regular bands then comes into play and has had a major influence on both the combinatorics and the probability theory associated to such structures. In a recent pa- per, the authors established a close connection between algebraic and combinatorial invariants of a left regular band by showing that certain homological invariants of the algebra of a left regular band coincide with the cohomology of order complexes of posets naturally associated to the left regular band. The purpose of the present monograph is to further develop and deepen the connection between left regular bands and poset topology. This allows us to compute finite projective resolutions of all simple mod- ules of unital left regular band algebras over fields and much more. In the process, we are led to define the class of CW left regular bands as the class of left regular bands whose associated posets are the face posets of regular CW complexes. Most of the examples that have arisen in the literature belong to this class. A new and important class of ex- amples is a left regular band structure on the face poset of a CAT(0) cube complex. -
Choosing Your Math Course
CHOOSING YOUR MATH COURSE When choosing your first math class, it’s important to consider your current skills, the topics covered, and course expectations. It’s also helpful to think about how each course aligns with both your interests and your career and transfer goals. If you have questions, talk with your advisor. The courses on page 1 are developmental-level and the courses on page 2 are college-level. • Developmental math courses (Foundations of Math and Math & Algebra for College) offer the chance to develop concepts and skills you may have forgotten or never had the chance to learn. They focus less on lecture and more on practicing concepts individually and in groups. Your homework will emphasize developing and practicing new skills. • College-level math courses build on foundational skills taught in developmental math courses. Homework, quizzes, and exams will often include word problems that require multiple steps and incorporate a variety of math skills. You should expect three to four exams per semester, which cover a variety of concepts, and shorter weekly quizzes which focus on one or two concepts. You may be asked to complete a final project and submit a paper using course concepts, research, and writing skills. Course Your Skills Readiness Topics Covered & Course Expectations Foundations of 1. All multiplication facts (through tens, preferably twelves) should be memorized. In Foundations of Mathematics, you will: Mathematics • learn to use frections, decimals, percentages, whole numbers, & 2. Whole number addition, subtraction, multiplication, division (without a calculator): integers to solve problems • interpret information that is communicated in a graph, chart & table Math & Algebra 1. -
Teaching Strategies for Improving Algebra Knowledge in Middle and High School Students
EDUCATOR’S PRACTICE GUIDE A set of recommendations to address challenges in classrooms and schools WHAT WORKS CLEARINGHOUSE™ Teaching Strategies for Improving Algebra Knowledge in Middle and High School Students NCEE 2015-4010 U.S. DEPARTMENT OF EDUCATION About this practice guide The Institute of Education Sciences (IES) publishes practice guides in education to provide edu- cators with the best available evidence and expertise on current challenges in education. The What Works Clearinghouse (WWC) develops practice guides in conjunction with an expert panel, combining the panel’s expertise with the findings of existing rigorous research to produce spe- cific recommendations for addressing these challenges. The WWC and the panel rate the strength of the research evidence supporting each of their recommendations. See Appendix A for a full description of practice guides. The goal of this practice guide is to offer educators specific, evidence-based recommendations that address the challenges of teaching algebra to students in grades 6 through 12. This guide synthesizes the best available research and shares practices that are supported by evidence. It is intended to be practical and easy for teachers to use. The guide includes many examples in each recommendation to demonstrate the concepts discussed. Practice guides published by IES are available on the What Works Clearinghouse website at http://whatworks.ed.gov. How to use this guide This guide provides educators with instructional recommendations that can be implemented in conjunction with existing standards or curricula and does not recommend a particular curriculum. Teachers can use the guide when planning instruction to prepare students for future mathemat- ics and post-secondary success. -
Taming the Unknown. a History of Algebra from Antiquity to the Early Twentieth Century, by Victor J
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 52, Number 4, October 2015, Pages 725–731 S 0273-0979(2015)01491-6 Article electronically published on March 23, 2015 Taming the unknown. A history of algebra from antiquity to the early twentieth century, by Victor J. Katz and Karen Hunger Parshall, Princeton University Press, Princeton and Oxford, 2014, xvi+485 pp., ISBN 978-0-691-14905-9, US $49.50 1. Algebra now Algebra has been a part of mathematics since ancient times, but only in the 20th century did it come to play a crucial role in other areas of mathematics. Algebraic number theory, algebraic geometry, and algebraic topology now cast a big shadow on their parent disciplines of number theory, geometry, and topology. And algebraic topology gave birth to category theory, the algebraic view of mathematics that is now a dominant way of thinking in almost all areas. Even analysis, in some ways the antithesis of algebra, has been affected. As long ago as 1882, algebra captured some important territory from analysis when Dedekind and Weber absorbed the Abel and Riemann–Roch theorems into their theory of algebraic function fields, now part of the foundations of algebraic geometry. Not all mathematicians are happy with these developments. In a speech in 2000, entitled Mathematics in the 20th Century, Michael Atiyah said: Algebra is the offer made by the devil to the mathematician. The devil says “I will give you this powerful machine, and it will answer any question you like. All you need to do is give me your soul; give up geometry and you will have this marvellous machine.” Atiyah (2001), p. -
Basic Algebra Review
SC_03213974739_rp08.qxd 1/15/08 9:15 AM Page 1 Algebra Review Exponents (continued) Polynomials (continued) Factoring (continued) Rational Expressions Rational Expressions Equations of Lines Quotient Rules FOIL Expansion for Multiplying Two To find the value(s) for which a rational (continued) TwoVariables (continued) Factoring Trinomials, Z Binomials expression is undefined, set the denominator If a 0, Leading Term Z x2 Intercepts 0 equal to 0 and solve the resulting equation. SIMPLIFYING COMPLEX FRACTIONS i. Zero exponent: a = 1 i. Multiply the first terms. 2 + + Z To find the x-intercept, let y = 0. To factor ax bx c, a 1: Lowest Terms Numbers Linear Equations ii. Multiply the outer terms. Method 1 To find the y-intercept, let x = 0. -n = 1 By Grouping To write a rational expression in lowest terms: ii. Negative exponents: a n iii. Multiply the inner terms. i. Simplify the numerator and denominator FRACTIONS Definition of Subtraction Properties a i. Find m and n such that i. Factor the numerator and denominator. Slope iv. Multiply the last terms. = + = separately. m mn ac and m n b. Suppose (x , y ) and (x , y ) are two differ- Addition and Subtraction x - y = x + -y i. Addition: The same quantity may be a - ii. Divide out common factors. ii. Divide by multiplying the simplified 1 1 2 2 1 2 iii. Quotient rule: = m n v. Collect like terms. Z added to (or subtracted from) each side of n a ii. Then numerator by the reciprocal of the ent points on a line. If x1 x2, then the i. -
Linear Algebra Methods in Combinatorics
Linear Algebra methods in Combinatorics Dani¨elKroes Jacob Naranjo Jiaxi Nie [email protected] [email protected] [email protected] Jason O'Neill Nicholas Sieger Sam Sprio Emily Zhu [email protected] [email protected] [email protected] [email protected] Fall 2019 Quarter Abstract Linear algebra tools have been used to solve many problems in extremal combinatorics. The far reaching nature of the subject matter has resulted in a book [4] written by Babai and Frankl. Many of the proofs in the area are short, elegant and straightforward and as a result fit perfectly in a graduate student seminar. We will start with a few results which are fundamental in extremal set theory including the oddtown/eventown problem and the Frankl-Wilson Theorem. The seminar will culminate with some big results in the past few years including the method of slice rank to solve capset problem and the recently solved sensitivity conjecture. 1 Introduction These notes are comprised from an eight lecture series for graduate students in combinatorics at UCSD during the Fall 2019 Quarter. The organization of these expository notes is as follows. Each section corresponds to a fifty minute lecture given as part of the seminar. We shall first establish some common notation. 1.1 Notation Throughout these notes, we let [n] := f1; 2; : : : ; ng and consider 2[n] := fA ⊂ [n]g: Moreover, we are interested in the collection of size k subsets of an n-element set which we denote [n] as k := fA ⊂ [n] : jAj = kg: [n] [n] We denote subfamilies of k and 2 by calligraphic letters, sets by capital letters and elements by lower case letters.