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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. -
Sunflower Theorems and Convex Codes
Sunflower Theorems and Convex Codes Amzi Jeffs University of Washington, Seattle UC Davis Algebra & Discrete Mathematics Seminar April 6th 2020 Part I: Place Cells and Convex Codes Place Cells 1971: O'Keefe and Dostrovsky describe place cells in the hippocampus of rats. 2019 video: https://youtu.be/puCV1grkdJA Main idea: Each place cell fires in a particular region. They \know" where the rat is. How much do they know? Mathematical Model of Place Cells 2013: Curto et al introduce convex neural codes. Index your place cells (neurons) by [n] def= f1; 2; : : : ; ng. d Each neuron i 2 [n] fires when rat is in convex open Ui in R . As rat moves, multiple neurons may fire at same time. Write down all the sets of neurons that fire together and get a convex neural code C ⊆ 2[n]. Example with 3 neurons in R2: Formal Definitions Definition A code is any subset of 2[n]. Elements of a code are codewords. Definition d Let U = fU1;:::;Ung be a collection of convex open sets in R . The code of U is def d code(U) = σ ⊆ [n] There is p 2 R with p 2 Ui , i 2 σ : The collection U is called a convex realization. Codes that have convex realizations are called convex. Notice! If the Ui correspond to place cells, we can compute code(U) from the brain directly (if the rat explores sufficiently...) Terminology in Practice Below, U = fU1;U2;U3g is a convex realization of C = f123; 12; 23; 2; 3; ;g. Therefore C is a convex code. -
APPM 1350 - Calculus 1
APPM 1350 - Calculus 1 Course Objectives: This class will form the basis for many of the standard skills required in all of Engineering, the Sciences, and Mathematics. Specically, students will: • Understand the concepts, techniques and applications of dierential and integral calculus including limits, rate of change of functions, and derivatives and integrals of algebraic and transcendental functions. • Improve problem solving and critical thinking Textbook: Essential Calculus, 2nd Edition by James Stewart. We will cover Chapters 1-5. You will also need an access code for WebAssign’s online homework system. The access code can also be purchased separately. Schedule and Topics Covered Day Section Topics 1 Appendix A Trigonometry 2 1.1 Functions and Their Representation 3 1.2 Essential Functions 4 1.3 The Limit of a Function 5 1.4 Calculating Limits 6 1.5 Continuity 7 1.5/1.6 Continuity/Limits Involving Innity 8 1.6 Limits Involving Innity 9 2.1 Derivatives and Rates of Change 10 Exam 1 Review Exam 1 Topics 11 2.2 The Derivative as a Function 12 2.3 Basic Dierentiation Formulas 13 2.4 Product and Quotient Rules 14 2.5 Chain Rule 15 2.6 Implicit Dierentiation 16 2.7 Related Rates 17 2.8 Linear Approximation and Dierentials 18 3.1 Max and Min Values 19 3.2 The Mean Value Theorem 20 3.3 Derivatives and Shapes of Graphs 21 3.4 Curve Sketching 22 Exam 2 Review Exam 2 Topics 23 3.4/3.5 Curve Sketching/Optimization Problems 24 3.5 Optimization Problems 25 3.6/3.7 Newton’s Method/Antiderivatives 26 3.7/Appendix B Sigma Notation 27 Appendix B, -
An Epsilon of Room, II: Pages from Year Three of a Mathematical Blog
An Epsilon of Room, II: pages from year three of a mathematical blog Terence Tao This is a preliminary version of the book An Epsilon of Room, II: pages from year three of a mathematical blog published by the American Mathematical Society (AMS). This preliminary version is made available with the permission of the AMS and may not be changed, edited, or reposted at any other website without explicit written permission from the author and the AMS. Author's preliminary version made available with permission of the publisher, the American Mathematical Society Author's preliminary version made available with permission of the publisher, the American Mathematical Society To Garth Gaudry, who set me on the road; To my family, for their constant support; And to the readers of my blog, for their feedback and contributions. Author's preliminary version made available with permission of the publisher, the American Mathematical Society Author's preliminary version made available with permission of the publisher, the American Mathematical Society Contents Preface ix A remark on notation x Acknowledgments xi Chapter 1. Expository articles 1 x1.1. An explicitly solvable nonlinear wave equation 2 x1.2. Infinite fields, finite fields, and the Ax-Grothendieck theorem 8 x1.3. Sailing into the wind, or faster than the wind 15 x1.4. The completeness and compactness theorems of first-order logic 24 x1.5. Talagrand's concentration inequality 43 x1.6. The Szemer´edi-Trotter theorem and the cell decomposition 50 x1.7. Benford's law, Zipf's law, and the Pareto distribution 58 x1.8. Selberg's limit theorem for the Riemann zeta function on the critical line 70 x1.9. -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
CHAPTER 3: Derivatives
CHAPTER 3: Derivatives 3.1: Derivatives, Tangent Lines, and Rates of Change 3.2: Derivative Functions and Differentiability 3.3: Techniques of Differentiation 3.4: Derivatives of Trigonometric Functions 3.5: Differentials and Linearization of Functions 3.6: Chain Rule 3.7: Implicit Differentiation 3.8: Related Rates • Derivatives represent slopes of tangent lines and rates of change (such as velocity). • In this chapter, we will define derivatives and derivative functions using limits. • We will develop short cut techniques for finding derivatives. • Tangent lines correspond to local linear approximations of functions. • Implicit differentiation is a technique used in applied related rates problems. (Section 3.1: Derivatives, Tangent Lines, and Rates of Change) 3.1.1 SECTION 3.1: DERIVATIVES, TANGENT LINES, AND RATES OF CHANGE LEARNING OBJECTIVES • Relate difference quotients to slopes of secant lines and average rates of change. • Know, understand, and apply the Limit Definition of the Derivative at a Point. • Relate derivatives to slopes of tangent lines and instantaneous rates of change. • Relate opposite reciprocals of derivatives to slopes of normal lines. PART A: SECANT LINES • For now, assume that f is a polynomial function of x. (We will relax this assumption in Part B.) Assume that a is a constant. • Temporarily fix an arbitrary real value of x. (By “arbitrary,” we mean that any real value will do). Later, instead of thinking of x as a fixed (or single) value, we will think of it as a “moving” or “varying” variable that can take on different values. The secant line to the graph of f on the interval []a, x , where a < x , is the line that passes through the points a, fa and x, fx. -
The Sunflower Lemma of Erdős and Rado
The Sunflower Lemma of Erdős and Rado René Thiemann March 1, 2021 Abstract We formally define sunflowers and provide a formalization ofthe sunflower lemma of Erdős and Rado: whenever a set ofsize-k-sets has a larger cardinality than (r − 1)k · k!, then it contains a sunflower of cardinality r. 1 Sunflowers Sunflowers are sets of sets, such that whenever an element is contained in at least two of the sets, then it is contained in all of the sets. theory Sunflower imports Main HOL−Library:FuncSet begin definition sunflower :: 0a set set ) bool where sunflower S = (8 x: (9 AB: A 2 S ^ B 2 S ^ A 6= B ^ x 2 A ^ x 2 B) −! (8 A: A 2 S −! x 2 A)) lemma sunflower-subset: F ⊆ G =) sunflower G =) sunflower F hproof i lemma pairwise-disjnt-imp-sunflower: pairwise disjnt F =) sunflower F hproof i lemma card2-sunflower: assumes finite S and card S ≤ 2 shows sunflower S hproof i lemma empty-sunflower: sunflower fg hproof i lemma singleton-sunflower: sunflower fAg hproof i 1 lemma doubleton-sunflower: sunflower fA;Bg hproof i lemma sunflower-imp-union-intersect-unique: assumes sunflower S and x 2 (S S) − (T S) shows 9 ! A: A 2 S ^ x 2 A hproof i lemma union-intersect-unique-imp-sunflower: V S T assumes x: x 2 ( S) − ( S) =) 9 ≤1 A: A 2 S ^ x 2 A shows sunflower S hproof i lemma sunflower-iff-union-intersect-unique: sunflower S ! (8 x 2 S S − T S: 9 ! A: A 2 S ^ x 2 A) (is ?l = ?r) hproof i lemma sunflower-iff-intersect-Uniq: T sunflower S ! (8 x: x 2 S _ (9 ≤1 A: A 2 S ^ x 2 A)) (is ?l = ?r) hproof i If there exists sunflowers whenever all elements are sets of thesame cardinality r, then there also exists sunflowers whenever all elements are sets with cardinality at most r. -
Coding for Sunflowers
Coding for Sunflowers Anup Rao University of Washington [email protected] January 8, 2020 Abstract A sunflower is a family of sets that have the same pairwise intersections. We simplify a recent result of Alweiss, Lovett, Wu and Zhang that gives an upper bound on the size of every family of sets of size k that does not contain a sunflower. We show how to use the converse of Shannon's noiseless coding theorem to give a cleaner proof of a similar bound. 1 Introduction A p-sunflower is a family of p sets whose pairwise intersections are identical. How large can a family of sets of size k be if the family does not contain a p-sunflower? Erd}osand Rado [ER60] were the first to pose and answer this question. They showed that any family with more than (p−1)k ·k! sets of size k must contain a p-sunflower. This fundamental fact has many applications in mathematics and computer science [ES92, Raz85, FMS97, GM07, GMR13, Ros14, RR18, LZ19, LSZ19]. After nearly 60 years, the correct answer to this question is still not known. There is a family of (p−1)k sets of size k that does not contain a p-sunflower, and Erd}osand Rado conjectured that their lemma could be improved to show that this is essentially the extremal example. Recently, Alweiss, Lovett, Wu and Zhang [ALWZ19] made substantial progress towards resolving the conjecture. They showed that (log k)k · (p log log k)O(k) sets ensure the presence of a p-sunflower. -
On Sunflowers and Matrix Multiplication
On Sunflowers and Matrix Multiplication Noga Alon ∗ Amir Shpilka y Christopher Umans z Abstract We present several variants of the sunflower conjecture of Erdos˝ and Rado [ER60] and discuss the relations among them. We then show that two of these conjectures (if true) imply negative answers to questions of Cop- persmith and Winograd [CW90] and Cohn et al [CKSU05] regarding possible approaches for obtaining fast matrix multiplication algorithms. Specifically, we show that the Erdos-Rado˝ sunflower conjecture (if true) implies a negative answer to the “no three disjoint equivoluminous subsets” question of Copper- Zn smith and Winograd [CW90]; we also formulate a “multicolored” sunflower conjecture in 3 and show that (if true) it implies a negative answer to the “strong USP” conjecture of [CKSU05] (although it does not seem to impact a second conjecture in [CKSU05] or the viability of the general group-theoretic ap- proach). A surprising consequence of our results is that the Coppersmith-Winograd conjecture actually implies the Cohn et al. conjecture. Zn The multicolored sunflower conjecture in 3 is a strengthening of the well-known (ordinary) sun- Zn flower conjecture in 3 , and we show via our connection that a construction from [CKSU05] yields a lower bound of (2:51 :::)n on the size of the largest multicolored 3-sunflower-free set, which beats the current best known lower bound of (2:21 :::)n [Edel04] on the size of the largest 3-sunflower-free set in Zn 3 . ∗Sackler School of Mathematics and Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel and Institute for Advanced Study, Princeton, New Jersey, 08540, USA. -
A Sunflower Anti-Ramsey Theorem and Its Applications
A SUNFLOWER ANTI-RAMSEY THEOREM AND ITS APPLICATIONS LEONARDO MART´INEZ-SANDOVAL, MIGUEL RAGGI, AND EDGARDO ROLDAN-PENSADO´ Abstract. A h-sunflower in a hypergraph is a family of edges with h vertices in common. We show that if we colour the edges of a complete hypergraph in such a way that any monochromatic h-sunflower has at most λ petals, then it contains a large rainbow complete subhypergraph. This extends a theorem by Lefmann, R¨odland Wysocka, but this version can be applied to problems in geometry and algebra. We also give an infinite version of the theorem. 1. Introduction Let Kk(V ) = (V; E) be the complete k-hypergraph on V . In other words, V V let V be a set and E = k , where k represents the family of subsets of size k of V . A colouring of Kk(V ) is simply a function ∆ : E ! N. We think of each i 2 N as a colour, so e has colour i if ∆(e) = i. If ∆ : E ! [c], then we call 0 V 0 it a c-colouring. A subset V of V is monochromatic if ∆( k ) = fig for some i 2 [c], and it is rainbow if ∆(e1) 6= ∆(e2) for every pair of distinct elements V 0 e1; e2 2 k . Ramsey's theorem [Ram30] guarantees the existence of monochromatic sub- sets in sufficiently large structures. There is a quote by Motzkin [Pr¨o05] that describes this behaviour: \Complete disorder is impossible." arXiv:1505.05170v1 [math.CO] 19 May 2015 |T. Motzkin. The precise statement of the theorem is as follows. -
A Gallery of Visualization DEMOS for Related Rate Problems The
A Gallery of Visualization DEMOS for Related Rate Problems The following is a gallery of demos for visualizing common related rate situations. These animations can be used by instructors in a classroom setting or by students to aid in acquiring a visualization background relating to the steps for solving related problems. Two file formats, gif and mov (QuickTime) are available. 1. The gif animations should run on most systems and the file sizes are relatively small. 2. The mov animations require the QuickTime Player which is a free download available by clicking here; these file are also small. (The mov files may not execute properly in old versions of QuickTime.) 3. The collection of animations in gif and mov format can be downloaded; see the 'bulk' zipped download category at the bottom of the following table. General problem Click to view the Animation Sample Click to view the gif file. description. QuickTime file. Click to see gif Click to see mov Filling a conical tank. animation animation Searchlight rotates to Click to see gif Click to see mov follow a walker. animation animation Filling a cylindrical tank, Click to see gif Click to see mov two ways. animation animation Airplane & observer. Click to see gif Click to see mov animation animation Click to see gif Click to see mov Conical sand pile. animation animation Click to see gif Click to see mov Sliding ladder. animation animation Click to see gif Click to see mov Raising ladder. animation animation Shadow of a walking Click to see gif Click to see mov figure. -
Math Flow Chart
Updated November 12, 2014 MATH FLOW CHART Grade 6 Grade 7 Grade 8 Grade 9 Grade 10 Grade 11 Grade 12 Calculus Advanced Advanced Advanced Math AB or BC Algebra II Algebra I Geometry Analysis MAP GLA-Grade 8 MAP EOC-Algebra I MAP - ACT AP Statistics College Calculus Algebra/ Algebra I Geometry Algebra II AP Statistics MAP GLA-Grade 8 MAP EOC-Algebra I Trigonometry MAP - ACT Math 6 Pre-Algebra Statistics Extension Extension MAP GLA-Grade 6 MAP GLA-Grade 7 or or Algebra II Math 6 Pre-Algebra Geometry MAP EOC-Algebra I College Algebra/ MAP GLA-Grade 6 MAP GLA-Grade 7 MAP - ACT Foundations Trigonometry of Algebra Algebra I Algebra II Geometry MAP GLA-Grade 8 Concepts Statistics Concepts MAP EOC-Algebra I MAP - ACT Geometry Algebra II MAP EOC-Algebra I MAP - ACT Discovery Geometry Algebra Algebra I Algebra II Concepts Concepts MAP - ACT MAP EOC-Algebra I Additional Full Year Courses: General Math, Applied Technical Mathematics, and AP Computer Science. Calculus III is available for students who complete Calculus BC before grade 12. MAP GLA – Missouri Assessment Program Grade Level Assessment; MAP EOC – Missouri Assessment Program End-of-Course Exam Mathematics Objectives Calculus Mathematical Practices 1. Make sense of problems and persevere in solving them. 2. Reason abstractly and quantitatively. 3. Construct viable arguments and critique the reasoning of others. 4. Model with mathematics. 5. Use appropriate tools strategically. 6. Attend to precision. 7. Look for and make use of structure. 8. Look for and express regularity in repeated reasoning. Linear and Nonlinear Functions • Write equations of linear and nonlinear functions in various formats.