ENGG 2440B Discrete Mathematics for Engineers Tutorial 8

Size: px
Start display at page:

Download "ENGG 2440B Discrete Mathematics for Engineers Tutorial 8"

Transcription

1 ENGG 440B Discrete Mathematics for Engineers Tutorial 8 Jiajin Li Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong jjli@se.cuhk.edu.hk November, 018 Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

2 Overview 1 Binomial Coefficient Combinatorial Proof Selected Examples Inclusion-Exclusion (IE) Principle Theorem Selected Examples Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, 018 / 15

3 Binomial Identities Theorem (Pascal s Identity) For any integers n, r 0 with 1 r n 1, ( ) ( ) ( ) n n 1 n 1 +. r r r 1 Theorem (Binomial Theorem) Let n 1 be an integer. For any real numbers x, y, (x + y) n n k0 ( ) n x k y n k. k Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

4 Combinatorial Proof of Pascal s Identity Proof. Counting problem: Let S be the set of all unordered selections of r elements from the n-element ground set {1,,, n}. LHS: S ( n r ). RHS: the sum of two terms, which suggests that the addition principle is at work S S 1 + S. S 1 selections of r elements from {1,,..., n} in which 1 does not appear. S selections of r elements from {1,,..., n} in which 1 appear. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

5 Combinatorial Proof of Binomial Theorem Proof. Consider the term x k y n k in the expansion of (x + y) n where 0 k n. Such a term can be obtained as follows: we need take an x from k of them ( ) and a y from the remaining n k of them. By definition, there are n ways to perform this task. Hence, the term k ( ) n x k y n k k appears in the expansion of (x + y) n. By summing the above over k 0, 1,..., n, we obtain the desired identity. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

6 Example Example Let n be a given integer. Give a combinatorial proof of the identity n k ( ) k ( ) n State clearly what you are trying to count and how you are counting it. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

7 Solution Proof. n k ( ) k ( ) n Counting problem: The RHS is simply choosing 3 numbers out of a set of n + 1 numbers. Out of the 3 numbers, the smallest can be either 1,, 3,, n, n 1. If the smallest number is k, then we can choose numbers from ( k + 1, k +,,) n + 1( to complete ) the set of 3 numbers. n + 1 (k + 1) + 1 n k + 1 There are wayes to do it. Hence we have, ( ) n n 1 ( ) n k + 1 k1 n k ( ) k. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

8 Example: Vandermonde s Identity Example (Vandermonde s Identity) Give a combinatorial proof of the identity ( ) m + n r r k0 ( ) ( ) m n. k r k Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

9 Solution: Vandermonde s Identity Proof. ( ) m + n r r k0 ( ) ( ) m n k r k Counting problem: The LHS counts the number of ways to choose a committee of r people from a group of m men and n women. The RHS counts the same setting according to cases depending on the number of men on the committees, which can be range from 0 to r. If there are t men, ( ) then there must be r t ( women. ) Since in such a case there are m n wayes to select men and ways to select the women, the t r t ( ) ( ) m n number of such committees is. The result now follows from t r t the rule of sum. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

10 Inclusion-Exclusion (IE) Principle Theorem In its general form, the principle of inclusion-exclusion states that for finite sets {S 1, S,, S m }, one has the identity S 1 S m S S m S i S j + 1 i<j m 1 i<j<k m S i S j S k + ( 1) m 1 S 1 S S m. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

11 Example Example Given 3 types of coins, how many ways can one select 0 coins so that no coin is selected more than 8 times? Rough Idea. Step 1: Formulate it as a mathematical counting problem. ( ) k + r 1 x 1 + x + + x r k, x i 0 r 1 Step: Apply the Inclusion-Exclusion (IE) Principle. Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

12 Solution Example Given 3 types of coins, how many ways can one select 0 coins so that no coin is selected more than 8 times? Proof. Step 1: Let x 1, x, x 3 be the number of three different type coins respectively. Then, x 1 + x + x 3 0, 0 x i 8, i 1,, 3. (1) Need: Count the number of integer solution to (1). First, let S be the set of integer solution to Note that S x 1 + x + x 3 0, x i 0, i 1,, 3. ( ) ( ) Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

13 Solution cont d Proof. Step: Remove the solutions in S that violate the upper bounds. Let S i be the set of integer solution to S i x 1 + x + x 3 0, x i 9, x j 0, i j. ( ) S 1 S is the set of integer solution to ( ) 13 78, i 1,, 3. x 1 + x + x 3 0, x 1 9, x 9, x 3 0 Similarly, S 1 S 3 S S 3 6 and S 1 S S 3 0. ( ) Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

14 Solution cont d Proof. By IE principle, Thus, S 1 S S 3 S S 1 S S 3, S 1 S S 3 S 1 + S + S 3 3 S 1 S + S 1 S S 3. S 1 S S 3 ( ) 3 ( ) ( ) Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

15 Thank you! Jiajin Li (CUHK) ENGG 440B Discrete Mathematics for Engineers Tutorial November 8, / 15

Inclusion and Exclusion Principle

Inclusion and Exclusion Principle Inclusion and Exclusion Principle Tutor: Zhang Qi Systems Engineering and Engineering Management qzhang@se.cuhk.edu.hk November 2014 Tutor: Zhang Qi (SEEM) Tutorial 7 November 2014 1 / 14 Review Theorem

More information

Tutorial 2 WANG PENG. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. September 28, 2017

Tutorial 2 WANG PENG. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. September 28, 2017 Tutorial 2 WANG PENG Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong September 28, 2017 WANG PENG (ENGG 2440B) Tutorial 2 September 28, 2017 1 / 17 Outline

More information

Part IA Numbers and Sets

Part IA Numbers and Sets Part IA Numbers and Sets Theorems Based on lectures by A. G. Thomason Notes taken by Dexter Chua Michaelmas 2014 These notes are not endorsed by the lecturers, and I have modified them (often significantly)

More information

Notes. Combinatorics. Combinatorics II. Notes. Notes. Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry. Spring 2006

Notes. Combinatorics. Combinatorics II. Notes. Notes. Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry. Spring 2006 Combinatorics Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry Spring 2006 Computer Science & Engineering 235 Introduction to Discrete Mathematics Sections 4.1-4.6 & 6.5-6.6 of Rosen cse235@cse.unl.edu

More information

Counting with Categories and Binomial Coefficients

Counting with Categories and Binomial Coefficients Counting with Categories and Binomial Coefficients CSE21 Winter 2017, Day 17 (B00), Day 12 (A00) February 22, 2017 http://vlsicad.ucsd.edu/courses/cse21-w17 When sum rule fails Rosen p. 392-394 Let A =

More information

Combinations and Probabilities

Combinations and Probabilities Combinations and Probabilities Tutor: Zhang Qi Systems Engineering and Engineering Management qzhang@se.cuhk.edu.hk November 2014 Tutor: Zhang Qi (SEEM) Tutorial 7 November 2014 1 / 16 Combination Review

More information

Counting. Math 301. November 24, Dr. Nahid Sultana

Counting. Math 301. November 24, Dr. Nahid Sultana Basic Principles Dr. Nahid Sultana November 24, 2012 Basic Principles Basic Principles The Sum Rule The Product Rule Distinguishable Pascal s Triangle Binomial Theorem Basic Principles Combinatorics: The

More information

CS 2336 Discrete Mathematics

CS 2336 Discrete Mathematics CS 2336 Discrete Mathematics Lecture 8 Counting: Permutations and Combinations 1 Outline Definitions Permutation Combination Interesting Identities 2 Definitions Selection and arrangement of objects appear

More information

Counting Methods. CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo

Counting Methods. CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo Counting Methods CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo c Xin He (University at Buffalo) CSE 191 Discrete Structures 1 / 48 Need for Counting The problem of counting

More information

Combinations. April 12, 2006

Combinations. April 12, 2006 Combinations April 12, 2006 Combinations, April 12, 2006 Binomial Coecients Denition. The number of distinct subsets with j elements that can be chosen from a set with n elements is denoted by ( n j).

More information

Combinatorial Analysis

Combinatorial Analysis Chapter 1 Combinatorial Analysis STAT 302, Department of Statistics, UBC 1 A starting example: coin tossing Consider the following random experiment: tossing a fair coin twice There are four possible outcomes,

More information

Intermediate Math Circles February 14, 2018 Contest Prep: Number Theory

Intermediate Math Circles February 14, 2018 Contest Prep: Number Theory Intermediate Math Circles February 14, 2018 Contest Prep: Number Theory Part 1: Prime Factorization A prime number is an integer greater than 1 whose only positive divisors are 1 and itself. An integer

More information

Binomial Coefficient Identities/Complements

Binomial Coefficient Identities/Complements Binomial Coefficient Identities/Complements CSE21 Fall 2017, Day 4 Oct 6, 2017 https://sites.google.com/a/eng.ucsd.edu/cse21-fall-2017-miles-jones/ permutation P(n,r) = n(n-1) (n-2) (n-r+1) = Terminology

More information

Some Review Problems for Exam 3: Solutions

Some Review Problems for Exam 3: Solutions Math 3355 Spring 017 Some Review Problems for Exam 3: Solutions I thought I d start by reviewing some counting formulas. Counting the Complement: Given a set U (the universe for the problem), if you want

More information

Discrete Probability

Discrete Probability Discrete Probability Counting Permutations Combinations r- Combinations r- Combinations with repetition Allowed Pascal s Formula Binomial Theorem Conditional Probability Baye s Formula Independent Events

More information

Counting Strategies: Inclusion-Exclusion, Categories

Counting Strategies: Inclusion-Exclusion, Categories Counting Strategies: Inclusion-Exclusion, Categories Russell Impagliazzo and Miles Jones Thanks to Janine Tiefenbruck http://cseweb.ucsd.edu/classes/sp16/cse21-bd/ May 4, 2016 A scheduling problem In one

More information

Introduction to Decision Sciences Lecture 11

Introduction to Decision Sciences Lecture 11 Introduction to Decision Sciences Lecture 11 Andrew Nobel October 24, 2017 Basics of Counting Product Rule Product Rule: Suppose that the elements of a collection S can be specified by a sequence of k

More information

Lecture 2. MATH3220 Operations Research and Logistics Jan. 8, Pan Li The Chinese University of Hong Kong. Integer Programming Formulations

Lecture 2. MATH3220 Operations Research and Logistics Jan. 8, Pan Li The Chinese University of Hong Kong. Integer Programming Formulations Lecture 2 MATH3220 Operations Research and Logistics Jan. 8, 2015 Pan Li The Chinese University of Hong Kong 2.1 Agenda 1 2 3 2.2 : a linear program plus the additional constraints that some or all of

More information

Indistinguishable objects in indistinguishable boxes

Indistinguishable objects in indistinguishable boxes Counting integer partitions 2.4 61 Indistinguishable objects in indistinguishable boxes When placing k indistinguishable objects into n indistinguishable boxes, what matters? We are partitioning the integer

More information

HW Solution 2 Due: July 10:39AM

HW Solution 2 Due: July 10:39AM ECS 35: Probability and Random Processes 200/ HW Solution 2 Due: July 9 @ 0:39AM Lecturer: Prapun Suksompong, Ph.D. Instructions (a) A part of ONE question will be graded. Of course, you do not know which

More information

Combinatorics Sec/on of Rosen Ques/ons

Combinatorics Sec/on of Rosen Ques/ons Combinatorics Sec/on 5.1 5.6 7.5 7.6 of Rosen Spring 2011 CSCE 235 Introduc5on to Discrete Structures Course web- page: cse.unl.edu/~cse235 Ques/ons: cse235@cse.unl.edu Mo5va5on Combinatorics is the study

More information

MATH 10B METHODS OF MATHEMATICS: CALCULUS, STATISTICS AND COMBINATORICS

MATH 10B METHODS OF MATHEMATICS: CALCULUS, STATISTICS AND COMBINATORICS MATH 10B METHODS OF MATHEMATICS: CALCULUS, STATISTICS AND COMBINATORICS Lior Pachter and Lawrence C. Evans Department of Mathematics University of California Berkeley, CA 94720 January 21, 2013 Lior Pachter

More information

Announcements. CSE 321 Discrete Structures. Counting. Counting Rules. Important cases of the Product Rule. Counting examples.

Announcements. CSE 321 Discrete Structures. Counting. Counting Rules. Important cases of the Product Rule. Counting examples. Announcements CSE 321 Discrete Structures Winter 2008 Lecture 16 Counting Readings Friday, Wednesday: Counting 6 th edition: 5.1, 5.2, 5.3, 5 th edition: 4.1, 4.2. 4.3 Lecture 16 video will be posted on

More information

{ 0! = 1 n! = n(n 1)!, n 1. n! =

{ 0! = 1 n! = n(n 1)!, n 1. n! = Summations Question? What is the sum of the first 100 positive integers? Counting Question? In how many ways may the first three horses in a 10 horse race finish? Multiplication Principle: If an event

More information

Properties of Probability

Properties of Probability Econ 325 Notes on Probability 1 By Hiro Kasahara Properties of Probability In statistics, we consider random experiments, experiments for which the outcome is random, i.e., cannot be predicted with certainty.

More information

Bijective Proofs with Spotted Tilings

Bijective Proofs with Spotted Tilings Brian Hopkins, Saint Peter s University, New Jersey, USA Visiting Scholar, Mahidol University International College Editor, The College Mathematics Journal MUIC Mathematics Seminar 2 November 2016 outline

More information

BRITISH COLUMBIA SECONDARY SCHOOL MATHEMATICS CONTEST, 2017 Junior Preliminary Problems & Solutions

BRITISH COLUMBIA SECONDARY SCHOOL MATHEMATICS CONTEST, 2017 Junior Preliminary Problems & Solutions BRITISH COLUMBIA SECONDARY SCHOOL MATHEMATICS CONTEST, 017 Junior Preliminary Problems & s 1. If x is a number larger than, which of the following expressions is the smallest? (A) /(x 1) (B) /x (C) /(x

More information

2 - Strings and Binomial Coefficients

2 - Strings and Binomial Coefficients November 14, 2017 2 - Strings and Binomial Coefficients William T. Trotter trotter@math.gatech.edu Basic Definition Let n be a positive integer and let [n] = {1, 2,, n}. A sequence of length n such as

More information

CS280, Spring 2004: Final

CS280, Spring 2004: Final CS280, Spring 2004: Final 1. [4 points] Which of the following relations on {0, 1, 2, 3} is an equivalence relation. (If it is, explain why. If it isn t, explain why not.) Just saying Yes or No with no

More information

Discrete Mathematics. Kishore Kothapalli

Discrete Mathematics. Kishore Kothapalli Discrete Mathematics Kishore Kothapalli 2 Chapter 4 Advanced Counting Techniques In the previous chapter we studied various techniques for counting and enumeration. However, there are several interesting

More information

Discrete Mathematics & Mathematical Reasoning Chapter 6: Counting

Discrete Mathematics & Mathematical Reasoning Chapter 6: Counting Discrete Mathematics & Mathematical Reasoning Chapter 6: Counting Kousha Etessami U. of Edinburgh, UK Kousha Etessami (U. of Edinburgh, UK) Discrete Mathematics (Chapter 6) 1 / 39 Chapter Summary The Basics

More information

Binomial and Poisson Probability Distributions

Binomial and Poisson Probability Distributions Binomial and Poisson Probability Distributions Esra Akdeniz March 3, 2016 Bernoulli Random Variable Any random variable whose only possible values are 0 or 1 is called a Bernoulli random variable. What

More information

Math.3336: Discrete Mathematics. Combinatorics: Basics of Counting

Math.3336: Discrete Mathematics. Combinatorics: Basics of Counting Math.3336: Discrete Mathematics Combinatorics: Basics of Counting Instructor: Dr. Blerina Xhabli Department of Mathematics, University of Houston https://www.math.uh.edu/ blerina Email: blerina@math.uh.edu

More information

Basic counting techniques. Periklis A. Papakonstantinou Rutgers Business School

Basic counting techniques. Periklis A. Papakonstantinou Rutgers Business School Basic counting techniques Periklis A. Papakonstantinou Rutgers Business School i LECTURE NOTES IN Elementary counting methods Periklis A. Papakonstantinou MSIS, Rutgers Business School ALL RIGHTS RESERVED

More information

Example. If 4 tickets are drawn with replacement from ,

Example. If 4 tickets are drawn with replacement from , Example. If 4 tickets are drawn with replacement from 1 2 2 4 6, what are the chances that we observe exactly two 2 s? Exactly two 2 s in a sequence of four draws can occur in many ways. For example, (

More information

Binomial Probability. Permutations and Combinations. Review. History Note. Discuss Quizzes/Answer Questions. 9.0 Lesson Plan

Binomial Probability. Permutations and Combinations. Review. History Note. Discuss Quizzes/Answer Questions. 9.0 Lesson Plan 9.0 Lesson Plan Discuss Quizzes/Answer Questions History Note Review Permutations and Combinations Binomial Probability 1 9.1 History Note Pascal and Fermat laid out the basic rules of probability in a

More information

ENGG5781 Matrix Analysis and Computations Lecture 8: QR Decomposition

ENGG5781 Matrix Analysis and Computations Lecture 8: QR Decomposition ENGG5781 Matrix Analysis and Computations Lecture 8: QR Decomposition Wing-Kin (Ken) Ma 2017 2018 Term 2 Department of Electronic Engineering The Chinese University of Hong Kong Lecture 8: QR Decomposition

More information

COUNTING. Solutions Manual. 2nd Edition. Counting Downloaded from by on 02/19/18. For personal use only.

COUNTING. Solutions Manual. 2nd Edition. Counting Downloaded from  by on 02/19/18. For personal use only. COUNTING Solutions Manual 2nd Edition This page intentionally left blank COUNTING Solutions Manual 2nd Edition Koh Khee Meng National University of Singapore, Singapore Tay Eng Guan Nanyang Technological

More information

Probability Theory and Statistics

Probability Theory and Statistics robability Theory and Statistics Kasper K. erthelsen, Dept. For Mathematical Sciences kkb@math.aau.dk Literature: Walpole, Myers, Myers & Ye: robability and Statistics for Engineers and Scientists, rentice

More information

Lecture 4: Counting, Pigeonhole Principle, Permutations, Combinations Lecturer: Lale Özkahya

Lecture 4: Counting, Pigeonhole Principle, Permutations, Combinations Lecturer: Lale Özkahya BBM 205 Discrete Mathematics Hacettepe University http://web.cs.hacettepe.edu.tr/ bbm205 Lecture 4: Counting, Pigeonhole Principle, Permutations, Combinations Lecturer: Lale Özkahya Resources: Kenneth

More information

A = , A 32 = n ( 1) i +j a i j det(a i j). (1) j=1

A = , A 32 = n ( 1) i +j a i j det(a i j). (1) j=1 Lecture Notes: Determinant of a Square Matrix Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk 1 Determinant Definition Let A [a ij ] be an

More information

Binomial Coefficients MATH Benjamin V.C. Collins, James A. Swenson MATH 2730

Binomial Coefficients MATH Benjamin V.C. Collins, James A. Swenson MATH 2730 MATH 2730 Benjamin V.C. Collins James A. Swenson Binomial coefficients count subsets Definition Suppose A = n. The number of k-element subsets in A is a binomial coefficient, denoted by ( n k or n C k

More information

Sets II MATH Sets II. Benjamin V.C. Collins, James A. Swenson MATH 2730

Sets II MATH Sets II. Benjamin V.C. Collins, James A. Swenson MATH 2730 MATH 2730 Sets II Benjamin V.C. Collins James A. Swenson New sets from old Suppose A and B are the sets of multiples of 2 and multiples of 5: A = {n Z : 2 n} = {..., 8, 6, 4, 2, 0, 2, 4, 6, 8,... } B =

More information

The Inclusion Exclusion Principle

The Inclusion Exclusion Principle The Inclusion Exclusion Principle 1 / 29 Outline Basic Instances of The Inclusion Exclusion Principle The General Inclusion Exclusion Principle Counting Derangements Counting Functions Stirling Numbers

More information

Lecture 3: Miscellaneous Techniques

Lecture 3: Miscellaneous Techniques Lecture 3: Miscellaneous Techniques Rajat Mittal IIT Kanpur In this document, we will take a look at few diverse techniques used in combinatorics, exemplifying the fact that combinatorics is a collection

More information

Econ 325: Introduction to Empirical Economics

Econ 325: Introduction to Empirical Economics Econ 325: Introduction to Empirical Economics Lecture 2 Probability Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall Ch. 3-1 3.1 Definition Random Experiment a process leading to an uncertain

More information

Probabilistic models

Probabilistic models Kolmogorov (Andrei Nikolaevich, 1903 1987) put forward an axiomatic system for probability theory. Foundations of the Calculus of Probabilities, published in 1933, immediately became the definitive formulation

More information

Permutations and Combinations

Permutations and Combinations Permutations and Combinations Permutations Definition: Let S be a set with n elements A permutation of S is an ordered list (arrangement) of its elements For r = 1,..., n an r-permutation of S is an ordered

More information

True/False. Math 10B with Professor Stankova Worksheet, Midterm #1; Friday, 2/16/2018 GSI name: Roy Zhao

True/False. Math 10B with Professor Stankova Worksheet, Midterm #1; Friday, 2/16/2018 GSI name: Roy Zhao Math 10B with Professor Stankova Worksheet, Midterm #1; Friday, 2/16/2018 GSI name: Roy Zhao True/False 1. True False Among the problems we considered in class, a multi-stage process can be encoded (and

More information

Linear Classification: Perceptron

Linear Classification: Perceptron Linear Classification: Perceptron Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong 1 / 18 Y Tao Linear Classification: Perceptron In this lecture, we will consider

More information

ENGG5781 Matrix Analysis and Computations Lecture 9: Kronecker Product

ENGG5781 Matrix Analysis and Computations Lecture 9: Kronecker Product ENGG5781 Matrix Analysis and Computations Lecture 9: Kronecker Product Wing-Kin (Ken) Ma 2017 2018 Term 2 Department of Electronic Engineering The Chinese University of Hong Kong Kronecker product and

More information

COUNTING. Its Principles and Techniques (5) byk M Koh andb PTan

COUNTING. Its Principles and Techniques (5) byk M Koh andb PTan COUNTING Its Principles and Techniques (5) byk M Koh andb PTan Dr Tan Ban Pin obtained his BSc with Honours in Mathematics in 1992 and PhD in 1997 from NUS. His area of research is graph theory. Professor

More information

Combinatorics & Discrete Probability Theory

Combinatorics & Discrete Probability Theory & Discrete Probability Theory CIS008-2 Logic and Foundations of Mathematics David Goodwin david.goodwin@perisic.com 11:00, Tuesday 28 th February 2012 Outline 1 Combinatorics Enumeration Permutations Combinations

More information

Poisson approximations

Poisson approximations Chapter 9 Poisson approximations 9.1 Overview The Binn, p) can be thought of as the distribution of a sum of independent indicator random variables X 1 + + X n, with {X i = 1} denoting a head on the ith

More information

Statistical Inference

Statistical Inference Statistical Inference Lecture 1: Probability Theory MING GAO DASE @ ECNU (for course related communications) mgao@dase.ecnu.edu.cn Sep. 11, 2018 Outline Introduction Set Theory Basics of Probability Theory

More information

Order Statistics and Distributions

Order Statistics and Distributions Order Statistics and Distributions 1 Some Preliminary Comments and Ideas In this section we consider a random sample X 1, X 2,..., X n common continuous distribution function F and probability density

More information

Probability 1 (MATH 11300) lecture slides

Probability 1 (MATH 11300) lecture slides Probability 1 (MATH 11300) lecture slides Márton Balázs School of Mathematics University of Bristol Autumn, 2015 December 16, 2015 To know... http://www.maths.bris.ac.uk/ mb13434/prob1/ m.balazs@bristol.ac.uk

More information

Linear Classification: Linear Programming

Linear Classification: Linear Programming Linear Classification: Linear Programming Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong 1 / 21 Y Tao Linear Classification: Linear Programming Recall the definition

More information

SERIES

SERIES SERIES.... This chapter revisits sequences arithmetic then geometric to see how these ideas can be extended, and how they occur in other contexts. A sequence is a list of ordered numbers, whereas a series

More information

are the q-versions of n, n! and . The falling factorial is (x) k = x(x 1)(x 2)... (x k + 1).

are the q-versions of n, n! and . The falling factorial is (x) k = x(x 1)(x 2)... (x k + 1). Lecture A jacques@ucsd.edu Notation: N, R, Z, F, C naturals, reals, integers, a field, complex numbers. p(n), S n,, b(n), s n, partition numbers, Stirling of the second ind, Bell numbers, Stirling of the

More information

Counting. Mukulika Ghosh. Fall Based on slides by Dr. Hyunyoung Lee

Counting. Mukulika Ghosh. Fall Based on slides by Dr. Hyunyoung Lee Counting Mukulika Ghosh Fall 2018 Based on slides by Dr. Hyunyoung Lee Counting Counting The art of counting is known as enumerative combinatorics. One tries to count the number of elements in a set (or,

More information

Math Fall Final Exam. Friday, 14 December Show all work for full credit. The problems are worth 6 points each.

Math Fall Final Exam. Friday, 14 December Show all work for full credit. The problems are worth 6 points each. Name: Math 50 - Fall 2007 Final Exam Friday, 4 December 2007 Show all work for full credit. The problems are worth 6 points each.. Find the number of subsets of S = {, 2,... 0} that contain exactly 5 elements,

More information

1 The Basic Counting Principles

1 The Basic Counting Principles 1 The Basic Counting Principles The Multiplication Rule If an operation consists of k steps and the first step can be performed in n 1 ways, the second step can be performed in n ways [regardless of how

More information

Combinatorics. But there are some standard techniques. That s what we ll be studying.

Combinatorics. But there are some standard techniques. That s what we ll be studying. Combinatorics Problem: How to count without counting. How do you figure out how many things there are with a certain property without actually enumerating all of them. Sometimes this requires a lot of

More information

Introduction to Decision Sciences Lecture 10

Introduction to Decision Sciences Lecture 10 Introduction to Decision Sciences Lecture 10 Andrew Nobel October 17, 2017 Mathematical Induction Given: Propositional function P (n) with domain N + Basis step: Show that P (1) is true Inductive step:

More information

Principles of Counting. Debdeep Mukhopadhyay IIT Madras

Principles of Counting. Debdeep Mukhopadhyay IIT Madras Principles of Counting Debdeep Mukhopadhyay IIT Madras Part-I The Sum Rule Two tasks T 1 and T 2 are to be performed. If the task T 1 can be performed in m different ways and if the task T 2 can be performed

More information

Some Review Problems for Exam 3: Solutions

Some Review Problems for Exam 3: Solutions Math 3355 Fall 018 Some Review Problems for Exam 3: Solutions I thought I d start by reviewing some counting formulas. Counting the Complement: Given a set U (the universe for the problem), if you want

More information

Number Theory and Counting Method. Divisors -Least common divisor -Greatest common multiple

Number Theory and Counting Method. Divisors -Least common divisor -Greatest common multiple Number Theory and Counting Method Divisors -Least common divisor -Greatest common multiple Divisors Definition n and d are integers d 0 d divides n if there exists q satisfying n = dq q the quotient, d

More information

1 Combinatorial Analysis

1 Combinatorial Analysis ECE316 Notes-Winter 217: A. K. Khandani 1 1 Combinatorial Analysis 1.1 Introduction This chapter deals with finding effective methods for counting the number of ways that things can occur. In fact, many

More information

Discrete Mathematics, Spring 2004 Homework 4 Sample Solutions

Discrete Mathematics, Spring 2004 Homework 4 Sample Solutions Discrete Mathematics, Spring 2004 Homework 4 Sample Solutions 4.2 #77. Let s n,k denote the number of ways to seat n persons at k round tables, with at least one person at each table. (The numbers s n,k

More information

Linear Classification: Linear Programming

Linear Classification: Linear Programming Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong Recall the definition of linear classification. Definition 1. Let R d denote the d-dimensional space where the domain

More information

CSCE 222 Discrete Structures for Computing. Review for Exam 2. Dr. Hyunyoung Lee !!!

CSCE 222 Discrete Structures for Computing. Review for Exam 2. Dr. Hyunyoung Lee !!! CSCE 222 Discrete Structures for Computing Review for Exam 2 Dr. Hyunyoung Lee 1 Strategy for Exam Preparation - Start studying now (unless have already started) - Study class notes (lecture slides and

More information

UNCORRECTED SAMPLE PAGES. Extension 1. The binomial expansion and. Digital resources for this chapter

UNCORRECTED SAMPLE PAGES. Extension 1. The binomial expansion and. Digital resources for this chapter 15 Pascal s In Chapter 10 we discussed the factoring of a polynomial into irreducible factors, so that it could be written in a form such as P(x) = (x 4) 2 (x + 1) 3 (x 2 + x + 1). In this chapter we will

More information

MATH 243E Test #3 Solutions

MATH 243E Test #3 Solutions MATH 4E Test # Solutions () Find a recurrence relation for the number of bit strings of length n that contain a pair of consecutive 0s. You do not need to solve this recurrence relation. (Hint: Consider

More information

Probability, For the Enthusiastic Beginner (Exercises, Version 1, September 2016) David Morin,

Probability, For the Enthusiastic Beginner (Exercises, Version 1, September 2016) David Morin, Chapter 8 Exercises Probability, For the Enthusiastic Beginner (Exercises, Version 1, September 2016) David Morin, morin@physics.harvard.edu 8.1 Chapter 1 Section 1.2: Permutations 1. Assigning seats *

More information

Coarse-grain Model of the SNAP Scheduling Problem: Problem specification description and Pseudo-boolean encoding

Coarse-grain Model of the SNAP Scheduling Problem: Problem specification description and Pseudo-boolean encoding Coarse-grain Model of the SNAP Scheduling Problem: Problem specification description and Pseudo-boolean encoding Geoff Pike, Donghan Kim and Alejandro Bugacov July 11, 2003 1 1 Problem Specification The

More information

Lecture 6: The Pigeonhole Principle and Probability Spaces

Lecture 6: The Pigeonhole Principle and Probability Spaces Lecture 6: The Pigeonhole Principle and Probability Spaces Anup Rao January 17, 2018 We discuss the pigeonhole principle and probability spaces. Pigeonhole Principle The pigeonhole principle is an extremely

More information

Support weight enumerators and coset weight distributions of isodual codes

Support weight enumerators and coset weight distributions of isodual codes Support weight enumerators and coset weight distributions of isodual codes Olgica Milenkovic Department of Electrical and Computer Engineering University of Colorado, Boulder March 31, 2003 Abstract In

More information

1 True/False. Math 10B with Professor Stankova Worksheet, Discussion #9; Thursday, 2/15/2018 GSI name: Roy Zhao

1 True/False. Math 10B with Professor Stankova Worksheet, Discussion #9; Thursday, 2/15/2018 GSI name: Roy Zhao Math 10B with Professor Stankova Worksheet, Discussion #9; Thursday, 2/15/2018 GSI name: Roy Zhao 1 True/False 1. True False When we solve a problem one way, it is not useful to try to solve it in a second

More information

Carleton University. Final Examination Fall DURATION: 2 HOURS No. of students: 195

Carleton University. Final Examination Fall DURATION: 2 HOURS No. of students: 195 Carleton University Final Examination Fall 15 DURATION: 2 HOURS No. of students: 195 Department Name & Course Number: Computer Science COMP 2804A Course Instructor: Michiel Smid Authorized memoranda: Calculator

More information

Advanced Counting Techniques. Chapter 8

Advanced Counting Techniques. Chapter 8 Advanced Counting Techniques Chapter 8 Chapter Summary Applications of Recurrence Relations Solving Linear Recurrence Relations Homogeneous Recurrence Relations Nonhomogeneous Recurrence Relations Divide-and-Conquer

More information

Fermat's Little Theorem

Fermat's Little Theorem Fermat's Little Theorem CS 2800: Discrete Structures, Spring 2015 Sid Chaudhuri Not to be confused with... Fermat's Last Theorem: x n + y n = z n has no integer solution for n > 2 Recap: Modular Arithmetic

More information

Solutions to Homework Problems

Solutions to Homework Problems Solutions to Homework Problems November 11, 2017 1 Problems II: Sets and Functions (Page 117-118) 11. Give a proof or a counterexample of the following statements: (vi) x R, y R, xy 0; (x) ( x R, y R,

More information

12.1 Chromatic Number

12.1 Chromatic Number 12.1 Chromatic Number History of Graph Colorings Theorem The Four Color Theorem: Any planar graph can be colored in at most four colors. But, it wasn t always the Four Color Theorem... Four Color Conjecture

More information

2.6 Tools for Counting sample points

2.6 Tools for Counting sample points 2.6 Tools for Counting sample points When the number of simple events in S is too large, manual enumeration of every sample point in S is tedious or even impossible. (Example) If S contains N equiprobable

More information

Contents. Counting Methods and Induction

Contents. Counting Methods and Induction Contents Counting Methods and Induction Lesson 1 Counting Strategies Investigations 1 Careful Counting... 555 Order and Repetition I... 56 3 Order and Repetition II... 569 On Your Own... 573 Lesson Counting

More information

CSC 344 Algorithms and Complexity. Proof by Mathematical Induction

CSC 344 Algorithms and Complexity. Proof by Mathematical Induction CSC 344 Algorithms and Complexity Lecture #1 Review of Mathematical Induction Proof by Mathematical Induction Many results in mathematics are claimed true for every positive integer. Any of these results

More information

CIVL Why are we studying probability and statistics? Learning Objectives. Basic Laws and Axioms of Probability

CIVL Why are we studying probability and statistics? Learning Objectives. Basic Laws and Axioms of Probability CIVL 3103 Basic Laws and Axioms of Probability Why are we studying probability and statistics? How can we quantify risks of decisions based on samples from a population? How should samples be selected

More information

Set theory background for probability

Set theory background for probability Set theory background for probability Defining sets (a very naïve approach) A set is a collection of distinct objects. The objects within a set may be arbitrary, with the order of objects within them having

More information

Lecture Notes 7 Random Processes. Markov Processes Markov Chains. Random Processes

Lecture Notes 7 Random Processes. Markov Processes Markov Chains. Random Processes Lecture Notes 7 Random Processes Definition IID Processes Bernoulli Process Binomial Counting Process Interarrival Time Process Markov Processes Markov Chains Classification of States Steady State Probabilities

More information

Number Theory: Niven Numbers, Factorial Triangle, and Erdos' Conjecture

Number Theory: Niven Numbers, Factorial Triangle, and Erdos' Conjecture Sacred Heart University DigitalCommons@SHU Mathematics Undergraduate Publications Mathematics -2018 Number Theory: Niven Numbers, Factorial Triangle, and Erdos' Conjecture Sarah Riccio Sacred Heart University,

More information

Foundations of Computer Science Lecture 14 Advanced Counting

Foundations of Computer Science Lecture 14 Advanced Counting Foundations of Computer Science Lecture 14 Advanced Counting Sequences with Repetition Union of Overlapping Sets: Inclusion-Exclusion Pigeonhole Principle Last Time To count complex objects, construct

More information

Probabilistic models

Probabilistic models Probabilistic models Kolmogorov (Andrei Nikolaevich, 1903 1987) put forward an axiomatic system for probability theory. Foundations of the Calculus of Probabilities, published in 1933, immediately became

More information

Binary codes of t-designs and Hadamard matrices

Binary codes of t-designs and Hadamard matrices Binary codes of t-designs and Hadamard matrices Akihiro Munemasa 1 1 Graduate School of Information Sciences Tohoku University November 8, 2013 JSPS-DST Asian Academic Seminar 2013 Discrete Mathematics

More information

From Simplest Recursion to the Recursion of Generalizations of Cross Polytope Numbers

From Simplest Recursion to the Recursion of Generalizations of Cross Polytope Numbers Kennesaw State University DigitalCommons@Kennesaw State University Honors College Capstones and Theses Honors College Spring 5-6-2017 From Simplest Recursion to the Recursion of Generalizations of Cross

More information

Using q-calculus to study LDL T factorization of a certain Vandermonde matrix

Using q-calculus to study LDL T factorization of a certain Vandermonde matrix Using q-calculus to study LDL T factorization of a certain Vandermonde matrix Alexey Kuznetsov May 2, 207 Abstract We use tools from q-calculus to study LDL T decomposition of the Vandermonde matrix V

More information

International General Certificate of Secondary Education CAMBRIDGE INTERNATIONAL EXAMINATIONS PAPER 1 MAY/JUNE SESSION 2002

International General Certificate of Secondary Education CAMBRIDGE INTERNATIONAL EXAMINATIONS PAPER 1 MAY/JUNE SESSION 2002 International General Certificate of Secondary Education CAMBRIDGE INTERNATIONAL EXAMINATIONS ADDITIONAL MATHEMATICS 0606/1 PAPER 1 MAY/JUNE SESSION 2002 2 hours Additional materials: Answer paper Electronic

More information

Chapter 7. Inclusion-Exclusion a.k.a. The Sieve Formula

Chapter 7. Inclusion-Exclusion a.k.a. The Sieve Formula Chapter 7. Inclusion-Exclusion a.k.a. The Sieve Formula Prof. Tesler Math 184A Fall 2019 Prof. Tesler Ch. 7. Inclusion-Exclusion Math 184A / Fall 2019 1 / 25 Venn diagram and set sizes A = {1, 2, 3, 4,

More information

Named numbres. Ngày 25 tháng 11 năm () Named numbres Ngày 25 tháng 11 năm / 7

Named numbres. Ngày 25 tháng 11 năm () Named numbres Ngày 25 tháng 11 năm / 7 Named numbres Ngày 25 tháng 11 năm 2011 () Named numbres Ngày 25 tháng 11 năm 2011 1 / 7 Fibonacci, Catalan, Stirling, Euler, Bernoulli Many sequences are famous. 1 1, 2, 3, 4,... the integers. () Named

More information

PROBLEMS ON CONGRUENCES AND DIVISIBILITY

PROBLEMS ON CONGRUENCES AND DIVISIBILITY PROBLEMS ON CONGRUENCES AND DIVISIBILITY 1. Do there exist 1,000,000 consecutive integers each of which contains a repeated prime factor? 2. A positive integer n is powerful if for every prime p dividing

More information