Basic Counting
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Section 4.2 Counting
Section 4.2 Counting CS 130 – Discrete Structures Counting: Multiplication Principle • The idea is to find out how many members are present in a finite set • Multiplication Principle: If there are n possible outcomes for a first event and m possible outcomes for a second event, then there are n*m possible outcomes for the sequence of two events. • From the multiplication principle, it follows that for 2 sets A and B, |A x B| = |A|x|B| – A x B consists of all ordered pairs with first component from A and second component from B CS 130 – Discrete Structures 27 Examples • How many four digit number can there be if repetitions of numbers is allowed? • And if repetition of numbers is not allowed? • If a man has 4 suits, 8 shirts and 5 ties, how many outfits can he put together? CS 130 – Discrete Structures 28 Counting: Addition Principle • Addition Principle: If A and B are disjoint events with n and m outcomes, respectively, then the total number of possible outcomes for event “A or B” is n+m • If A and B are disjoint sets, then |A B| = |A| + |B| using the addition principle • Example: A customer wants to purchase a vehicle from a dealer. The dealer has 23 autos and 14 trucks in stock. How many selections does the customer have? CS 130 – Discrete Structures 29 More On Addition Principle • If A and B are disjoint sets, then |A B| = |A| + |B| • Prove that if A and B are finite sets then |A-B| = |A| - |A B| and |A-B| = |A| - |B| if B A (A-B) (A B) = (A B) (A B) = A (B B) = A U = A Also, A-B and A B are disjoint sets, therefore using the addition principle, |A| = | (A-B) (A B) | = |A-B| + |A B| Hence, |A-B| = |A| - |A B| If B A, then A B = B Hence, |A-B| = |A| - |B| CS 130 – Discrete Structures 30 Using Principles Together • How many four-digit numbers begin with a 4 or a 5 • How many three-digit integers (numbers between 100 and 999 inclusive) are even? • Suppose the last four digit of a telephone number must include at least one repeated digit. -
180: Counting Techniques
180: Counting Techniques In the following exercise we demonstrate the use of a few fundamental counting principles, namely the addition, multiplication, complementary, and inclusion-exclusion principles. While none of the principles are particular complicated in their own right, it does take some practice to become familiar with them, and recognise when they are applicable. I have attempted to indicate where alternate approaches are possible (and reasonable). Problem: Assume n ≥ 2 and m ≥ 1. Count the number of functions f :[n] ! [m] (i) in total. (ii) such that f(1) = 1 or f(2) = 1. (iii) with minx2[n] f(x) ≤ 5. (iv) such that f(1) ≥ f(2). (v) that are strictly increasing; that is, whenever x < y, f(x) < f(y). (vi) that are one-to-one (injective). (vii) that are onto (surjective). (viii) that are bijections. (ix) such that f(x) + f(y) is even for every x; y 2 [n]. (x) with maxx2[n] f(x) = minx2[n] f(x) + 1. Solution: (i) A function f :[n] ! [m] assigns for every integer 1 ≤ x ≤ n an integer 1 ≤ f(x) ≤ m. For each integer x, we have m options. As we make n such choices (independently), the total number of functions is m × m × : : : m = mn. (ii) (We assume n ≥ 2.) Let A1 be the set of functions with f(1) = 1, and A2 the set of functions with f(2) = 1. Then A1 [ A2 represents those functions with f(1) = 1 or f(2) = 1, which is precisely what we need to count. We have jA1 [ A2j = jA1j + jA2j − jA1 \ A2j. -
UNIT 1: Counting and Cardinality
Grade: K Unit Number: 1 Unit Name: Counting and Cardinality Instructional Days: 40 days EVERGREEN SCHOOL K DISTRICT GRADE Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Counting Operations Geometry Measurement Numbers & Algebraic Thinking and and Data Operations in Cardinality Base 10 8 weeks 4 weeks 8 weeks 3 weeks 4 weeks 5 weeks UNIT 1: Counting and Cardinality Dear Colleagues, Enclosed is a unit that addresses all of the Common Core Counting and Cardinality standards for Kindergarten. We took the time to analyze, group and organize them into a logical learning sequence. Thank you for entrusting us with the task of designing a rich learning experience for all students, and we hope to improve the unit as you pilot it and make it your own. Sincerely, Grade K Math Unit Design Team CRITICAL THINKING COLLABORATION COMMUNICATION CREATIVITY Evergreen School District 1 MATH Curriculum Map aligned to the California Common Core State Standards 7/29/14 Grade: K Unit Number: 1 Unit Name: Counting and Cardinality Instructional Days: 40 days UNIT 1 TABLE OF CONTENTS Overview of the grade K Mathematics Program . 3 Essential Standards . 4 Emphasized Mathematical Practices . 4 Enduring Understandings & Essential Questions . 5 Chapter Overviews . 6 Chapter 1 . 8 Chapter 2 . 9 Chapter 3 . 10 Chapter 4 . 11 End-of-Unit Performance Task . 12 Appendices . 13 Evergreen School District 2 MATH Curriculum Map aligned to the California Common Core State Standards 7/29/14 Grade: K Unit Number: 1 Unit Name: Counting and Cardinality Instructional Days: 40 days Overview of the Grade K Mathematics Program UNIT NAME APPROX. -
STRUCTURE ENUMERATION and SAMPLING Chemical Structure Enumeration and Sampling Have Been Studied by Mathematicians, Computer
STRUCTURE ENUMERATION AND SAMPLING MARKUS MERINGER To appear in Handbook of Chemoinformatics Algorithms Chemical structure enumeration and sampling have been studied by 5 mathematicians, computer scientists and chemists for quite a long time. Given a molecular formula plus, optionally, a list of structural con- straints, the typical questions are: (1) How many isomers exist? (2) Which are they? And, especially if (2) cannot be answered completely: (3) How to get a sample? 10 In this chapter we describe algorithms for solving these problems. The techniques are based on the representation of chemical compounds as molecular graphs (see Chapter 2), i.e. they are mainly applied to constitutional isomers. The major problem is that in silico molecular graphs have to be represented as labeled structures, while in chemical 15 compounds, the atoms are not labeled. The mathematical concept for approaching this problem is to consider orbits of labeled molecular graphs under the operation of the symmetric group. We have to solve the so–called isomorphism problem. According to our introductory questions, we distinguish several dis- 20 ciplines: counting, enumerating and sampling isomers. While counting only delivers the number of isomers, the remaining disciplines refer to constructive methods. Enumeration typically encompasses exhaustive and non–redundant methods, while sampling typically lacks these char- acteristics. However, sampling methods are sometimes better suited to 25 solve real–world problems. There is a wide range of applications where counting, enumeration and sampling techniques are helpful or even essential. Some of these applications are closely linked to other chapters of this book. Counting techniques deliver pure chemical information, they can help to estimate 30 or even determine sizes of chemical databases or compound libraries obtained from combinatorial chemistry. -
Basic Combinatorics
Basic Combinatorics Carl G. Wagner Department of Mathematics The University of Tennessee Knoxville, TN 37996-1300 Contents List of Figures iv List of Tables v 1 The Fibonacci Numbers From a Combinatorial Perspective 1 1.1 A Simple Counting Problem . 1 1.2 A Closed Form Expression for f(n) . 2 1.3 The Method of Generating Functions . 3 1.4 Approximation of f(n) . 4 2 Functions, Sequences, Words, and Distributions 5 2.1 Multisets and sets . 5 2.2 Functions . 6 2.3 Sequences and words . 7 2.4 Distributions . 7 2.5 The cardinality of a set . 8 2.6 The addition and multiplication rules . 9 2.7 Useful counting strategies . 11 2.8 The pigeonhole principle . 13 2.9 Functions with empty domain and/or codomain . 14 3 Subsets with Prescribed Cardinality 17 3.1 The power set of a set . 17 3.2 Binomial coefficients . 17 4 Sequences of Two Sorts of Things with Prescribed Frequency 23 4.1 A special sequence counting problem . 23 4.2 The binomial theorem . 24 4.3 Counting lattice paths in the plane . 26 5 Sequences of Integers with Prescribed Sum 28 5.1 Urn problems with indistinguishable balls . 28 5.2 The family of all compositions of n . 30 5.3 Upper bounds on the terms of sequences with prescribed sum . 31 i CONTENTS 6 Sequences of k Sorts of Things with Prescribed Frequency 33 6.1 Trinomial Coefficients . 33 6.2 The trinomial theorem . 35 6.3 Multinomial coefficients and the multinomial theorem . 37 7 Combinatorics and Probability 39 7.1 The Multinomial Distribution . -
Polynomial Statistics, Necklace Polynomials, and the Arithmetic Dynamical Mordell-Lang Conjecture By
Polynomial Statistics, Necklace Polynomials, and the Arithmetic Dynamical Mordell-Lang Conjecture by Trevor Hyde A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Mathematics) in The University of Michigan 2019 Doctoral Committee: Professor Jeffrey Lagarias, Co-Chair Professor Michael Zieve, Co-Chair Professor Kartik Prasanna Professor John Schotland Professor John Stembridge Trevor Hyde [email protected] ORCID 0000-0002-9380-1928 © Trevor Hyde 2019 Dedication This thesis is dedicated to Aarthi and my parents Steve and Kathy. Thank you for all the years of unwavering love and support. ii Acknowledgments The author is grateful for the guidance and encouragement of his advisers Jeff Lagarias and Mike Zieve. I am also happy to thank Weiyan Chen, Benson Farb, Nir Gadish, Jonathan Gerhard, Ofir Gorodetsky, Rafe Jones, Bob Lutz, Andy Odesky, Karen Smith, Sheila Sundaram and Phil Tosteson for their help with and interest in my work over the past six years. The author was supported by NSF grant DMS-1162181 in the summer of 2017, the Rackham One-Term Dissertation Fellowship in the winter of 2017, the NSF grant DMS- 1701576 in the summer of 2018, and the Rackham Predoctoral Fellowship for the 2018-19 academic year. iii List of Figures 4.1 2-colorings of S3............................... 96 4.2 All primitive 2-colorings of S3........................ 96 iv List of Tables 2.1 Expected values of quadratic excess. 20 3.1 Low degree terms of M3;n¹qº......................... 35 3.2 First moments of linear factor statistic. 39 v Table of Contents Dedication . ii Acknowledgements . -
Fast Algorithms to Generate Necklaces, Unlabeled Necklaces, and Irreducible Polynomials Over GF(2)
Journal of Algorithms 37, 267᎐282Ž. 2000 doi:10.1006rjagm.2000.1108, available online at http:rrwww.idealibrary.com on Fast Algorithms to Generate Necklaces, Unlabeled Necklaces, and Irreducible Polynomials over GFŽ. 2 1 Kevin Cattell HP Research Labs, Santa Rosa, California E-mail: kevin᎐ [email protected] Frank Ruskey, Joe Sawada, and Micaela Serra Department of Computer Science, Uni¨ersity of Victoria, Victoria, British Columbia, Canada E-mail: [email protected]; [email protected], [email protected] and C. Robert Miers Department of Mathematics and Statistics, Uni¨ersity of Victoria, Victoria, British Columbia, Canada E-mail: [email protected] Received August 22, 1998 Many applications call for exhaustive lists of strings subject to various con- straints, such as inequivalence under group actions. A k-ary necklace is an equivalence class of k-ary strings under rotationŽ. the cyclic group . A k-ary unlabeled necklace is an equivalence class of k-ary strings under rotation and permutation of alphabet symbols. We present new, fast, simple, recursive algo- rithms for generatingŽ. i.e., listing all necklaces and binary unlabeled necklaces. These algorithms have optimal running times in the sense that their running times are proportional to the number of necklaces produced. The algorithm for generat- ing necklaces can be used as the basis for efficiently generating many other equivalence classes of strings under rotation and has been applied to generating bracelets, fixed density necklaces, and chord diagrams. As another application, we describe the implementation of a fast algorithm for listing all degree n irreducible and primitive polynomials over GFŽ. -
Methods of Enumeration of Microorganisms
Microbiology BIOL 275 ENUMERATION OF MICROORGANISMS I. OBJECTIVES • To learn the different techniques used to count the number of microorganisms in a sample. • To be able to differentiate between different enumeration techniques and learn when each should be used. • To have more practice in serial dilutions and calculations. II. INTRODUCTION For unicellular microorganisms, such as bacteria, the reproduction of the cell reproduces the entire organism. Therefore, microbial growth is essentially synonymous with microbial reproduction. To determine rates of microbial growth and death, it is necessary to enumerate microorganisms, that is, to determine their numbers. It is also often essential to determine the number of microorganisms in a given sample. For example, the ability to determine the safety of many foods and drugs depends on knowing the levels of microorganisms in those products. A variety of methods has been developed for the enumeration of microbes. These methods measure cell numbers, cell mass, or cell constituents that are proportional to cell number. The four general approaches used for estimating the sizes of microbial populations are direct and indirect counts of cells and direct and indirect measurements of microbial biomass. Each method will be described in more detail below. 1. Direct Count of Cells Cells are counted directly under the microscope or by an electronic particle counter. Two of the most common procedures used in microbiology are discussed below. Direct Count Using a Counting Chamber Direct microscopic counts are performed by spreading a measured volume of sample over a known area of a slide, counting representative microscopic fields, and relating the averages back to the appropriate volume-area factors. -
Unit 1: Counting and Cardinality Goal: Students Will Learn Number Names, the Counting Sequence, How to Count to Tell the Number of Objects, and How to Compare Numbers
Kindergarten – Trimester 1: Aug – Oct (11 weeks) Unit 1: Counting and Cardinality Goal: Students will learn number names, the counting sequence, how to count to tell the number of objects, and how to compare numbers. Students will represent and use whole numbers, initially with a set of objects. Students will learn to describe shapes and space. Structures to Support CA Content Standards/CGI/Problem Solving: Real World Math, Problem Analysis “Think Time”, Partner Collaboration, Productive Struggle, Whole Group Student Share Youcubed Week of Inspirational Math https://www.youcubed.org/week-inspirational-math/ Common Unit Tasks and Critical Areas of Instruction Core Curriculum Work Assessments/Proficiency Scale(s): Critical Concepts: Focus Areas (use these activities most of the time): Trimester 1 Tasks & Assessments: ● Cognitively Guided Counting and Cardinality: -Counting Collections Instruction ● MyMath Ch 1: Numbers 0 to 5 -Describe, Draw, Describe ● Counting Collections ● MyMath Ch 2: Numbers to 10 -Card games ● Word Problems (Separate – ● MyMath Ch 3: Numbers Beyond 10 -CGI Assessment (do in BOY, MOY, and EOY Result Unknown, Join – ● Counting Collections: What is it? to gauge student growth) Result Unknown, Partitive ● Questions for Counting Collections ● CGI Assessment Launch Script Division) ● Number Talks/Math Warm Ups (Ordinal numbers, compare ● CGI Kinder intro Assessment A ● Name, recognize, count, and numbers) ● K-1 CGI assess codebook write numbers 0 to 20 Operations/Algebraic Thinking: ● Compare numbers to 10 ● Word Problems -
Counting: Permutations, Combinations
CS 441 Discrete Mathematics for CS Lecture 17 Counting Milos Hauskrecht [email protected] 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Counting • Assume we have a set of objects with certain properties • Counting is used to determine the number of these objects Examples: • Number of available phone numbers with 7 digits in the local calling area • Number of possible match starters (football, basketball) given the number of team members and their positions CS 441 Discrete mathematics for CS M. Hauskrecht 1 Basic counting rules • Counting problems may be hard, and easy solutions are not obvious • Approach: – simplify the solution by decomposing the problem • Two basic decomposition rules: – Product rule • A count decomposes into a sequence of dependent counts (“each element in the first count is associated with all elements of the second count”) – Sum rule • A count decomposes into a set of independent counts (“elements of counts are alternatives”) CS 441 Discrete mathematics for CS M. Hauskrecht Inclusion-Exclusion principle Used in counts where the decomposition yields two count tasks with overlapping elements • If we used the sum rule some elements would be counted twice Inclusion-exclusion principle: uses a sum rule and then corrects for the overlapping elements. We used the principle for the cardinality of the set union. •|A B| = |A| + |B| - |A B| U B A CS 441 Discrete mathematics for CS M. Hauskrecht 2 Inclusion-exclusion principle Example: How many bitstrings of length 8 start either with a bit 1 or end with 00? • Count strings that start with 1: • How many are there? 27 • Count the strings that end with 00. -
Counting and Sizes of Sets Learning Goals
Counting and Sizes of Sets Learning goals: students determine how to tell when two sets are the same \size" and start looking at different sizes of sets. Two sets are called similar, written A ∼ B if there is a one-to-one and onto function whose domain is A and whose image is B. The function is a one-to-one correspondence and we call such functions bijections. Clearly the inverse of such a function is also such a function, so if A ∼ B then B ∼ A. Also, A ∼ A using the identity function. Other words often used for similar are equinumerous, equipollent, and equipotent. It shouldn't be too hard to prove that if A ∼ B and B ∼ C then A ∼ C. So similarity possesses the qualities of reflexivity, symmetry, and transitivity. We call such relationships equivalence relations. Equivalence relations seek to classify some kind of object. In this case, we are classifying sets by size, and all sets that a similar to A are the same size as A. The fancy word for \size" is cardinality. If n is a positive integer, a set A will have n elements in it if and only if A ∼ f1; 2; 3; : : : ; ng. (This actually requires some proof! We need to show that different values of n give sets that are not similar.) We also say that A has cardinal number n. It should be easy to prove that if f1; 2; : : : ; mg ∼ f1; 2; : : : ; ng then m = n. It should also be clear that if A ∼ ; then A = ;. The empty set will have cardinal number zero. -
MA241 Combinatorics
MA241 Combinatorics Keith Ball Books Bender and Williamson, Foundations of Combinatorics with Appli- cations. Harris, Hirst and Mossinghoff, Combinatorics and Graph Theory. Bollob´as, Graph Theory: An Introductory Course. Ball, Strange Curves, Counting Rabbits,... Cameron, Combinatorics: Topics, Techniques, Algorithms. Chapter 0. Introduction Combinatorics is not an easy subject to define: the syllabus doesn't do so. Combinatorial problems tend to deal with finite structures and frequently involve counting something. Instead of defining it I will give an example of the kind of arguments we shall use. It is a famous fact that if you select 23 people at random, there is a roughly 50:50 chance that some pair of them share a birthday: that two of the 23 people celebrate their birthdays on the same day. Let us confirm this. We shall make a simplifying assumption that there are 365 days in every year. It is easier to calculate the probability that all the people have different birthdays, and then subtract the result from 1. Consider the first person, Alice. Her birthday can fall on any day of the year. Now look at the second, Bob. If he is not to share his birthday with Alice, there are only 364 of the 365 dates available for 364 his birthday. The chance that he was born on one of those is 365. Now take the third person, Carol. If she is to avoid the birthdays of both Alice and Bob, she has only 363 possible days. So the chance 363 that she falls into one of them is 365. Hence the chance that these three people are born on different days of the year is 365 364 363 × × : 365 365 365 Continuing in this way for 23 people we get the probability that all 23 are born on different days to be 365 364 363 365 − 22 × × ::: × : 365 365 365 365 With a calculator or computer you can check that this number is about 1=2.