Today’s topics Counting Reading: Sections , 4.4 Upcoming

Slides:



Advertisements
Similar presentations
5.3 Permutations and Combinations
Advertisements

Practice Quiz 3 Recursive Definitions Relations Basic Counting Pigeonhole Principle Permutations & Combinations Discrete Probability.
1 Section 4.3 Permutations & Combinations. 2 Permutation Set of distinct objects in an ordered arrangement An ordered arrangement of r members of a set.
Permutations and Combinations Rosen 4.3. Permutations A permutation of a set of distinct objects is an ordered arrangement these objects. An ordered arrangement.
Counting Chapter 6 With Question/Answer Animations.
1 Combinatorics Rosen 6 th ed., § , § Combinatorics Count the number of ways to put things together into various combinations.Count the number.
CSE115/ENGR160 Discrete Mathematics 04/17/12
Permutations r-permutation (AKA “ordered r-selection”) An ordered arrangement of r elements of a set of n distinct elements. permutation of a set of n.
Combinations We should use permutation where order matters
Recursive Definitions Rosen, 3.4 Recursive (or inductive) Definitions Sometimes easier to define an object in terms of itself. This process is called.
Chapter 8 Counting Techniques PASCAL’S TRIANGLE AND THE BINOMIAL THEOREM.
Combinatorics 3/15 and 3/ Counting A restaurant offers the following menu: Main CourseVegetablesBeverage BeefPotatoesMilk HamGreen BeansCoffee.
Counting. Why counting  Determine the complexity of algorithms To sort n numbers, how many instructions are executed ?  Count the number of objects.
Chapter The Basics of Counting 5.2 The Pigeonhole Principle
Binomial Coefficients, Inclusion-exclusion principle
Chapter 6 With Question/Answer Animations 1. Chapter Summary The Basics of Counting The Pigeonhole Principle Permutations and Combinations Binomial Coefficients.
Permutations and Combinations
Chapter 3 Permutations and combinations
Dr. Eng. Farag Elnagahy Office Phone: King ABDUL AZIZ University Faculty Of Computing and Information Technology CPCS 222.
ICS 253: Discrete Structures I Counting and Applications King Fahd University of Petroleum & Minerals Information & Computer Science Department.
Chapter 5 The Binomial Coefficients
Section 3 – Permutations & Combinations. Learning Objectives Differentiate between: – Permutations: Ordering “r” of “n” objects (no replacement) Special.
Based on Rosen, Discrete Mathematics & Its Applications, 5e Prepared by (c) Michael P. Frank Modified by (c) Haluk Bingöl 1/37 Module.
Module #15: Combinatorics Rosen 5 th ed., §§ & §6.5 Now we are moving on to Ch. 4 It is the study of arrangement of objects e.g. enumeration, counting.
The Pigeonhole Principle. The pigeonhole principle Suppose a flock of pigeons fly into a set of pigeonholes to roost If there are more pigeons than pigeonholes,
1 Melikyan/DM/Fall09 Discrete Mathematics Ch. 6 Counting and Probability Instructor: Hayk Melikyan Today we will review sections 6.6,
Discrete Structures Combinations. Order Doesn’t Matter In the previous section, we looked at two cases where order matters: Multiplication Principle –
3.2 Combinations.
CS Lecture 8 Developing Your Counting Muscles.
1 Chapter 2 Pigeonhole Principle. 2 Summary Pigeonhole principle –simple form Pigeonhole principle –strong form Ramsey’s theorem.
L14: Permutations, Combinations and Some Review EECS 203: Discrete Mathematics.
Section The Pigeonhole Principle If a flock of 20 pigeons roosts in a set of 19 pigeonholes, one of the pigeonholes must have more than 1 pigeon.
ICS 253: Discrete Structures I Counting and Applications King Fahd University of Petroleum & Minerals Information & Computer Science Department.
11.1 CompSci 102© Michael Frank Today’s topics CountingCounting –Generalized Pigeonhole Principle –Permutations –Combinations –Binomial Coefficients –Writing.
COUNTING Discrete Math Team KS MATEMATIKA DISKRIT (DISCRETE MATHEMATICS ) 1.
Section 6.3. Section Summary Permutations Combinations.
L14: Permutations, Combinations and Some Review
The Pigeonhole Principle
Developing Your Counting Muscles
Permutations and Combinations
CSC 321: Data Structures Fall 2015 Counting and problem solving
CSE15 Discrete Mathematics 04/19/17
ICS 253: Discrete Structures I
Chapter 5, Section 5.1 The Basics of Counting
Discrete Structures for Computer Science
COCS DISCRETE STRUCTURES
CSC 321: Data Structures Fall 2016 Counting and proofs
12.2 Combinations and Binomial Theorem
Permutations and Combinations
Use Combinations and the Binomial Theorem
The Pigeonhole Principle
Permutations and Combinations
Permutations and Combinations
Counting Chapter 6 With Question/Answer Animations
CS 2210 Discrete Structures Counting
CS100: Discrete structures
Math/CSE 1019: Discrete Mathematics for Computer Science Fall 2011
Module #15: Combinatorics
Permutations and Combinations
Basic Counting.
Discrete Mathematics and its Applications
CSC 321: Data Structures Fall 2018 Counting and proofs
Counting techniques Basic Counting Principles, Pigeonhole Principle, Permutations and Combinations.
Module #15: Combinatorics
Basic Counting Lecture 9: Nov 5, 6.
Permutations and Combinations
Sequences and the Binomial Theorem
The Pigeonhole Principle
Discrete Mathematics and its Applications
CMPS 2433 Chapter 8 Counting Techniques
Presentation transcript:

Today’s topics Counting Reading: Sections 4.2-4.3, 4.4 Upcoming Generalized Pigeonhole Principle Permutations Combinations Binomial Coefficients Reading: Sections 4.2-4.3, 4.4 Upcoming Probability CompSci 102 © Michael Frank

Generalized Pigeonhole Principle If N objects are assigned to k places, then at least one place must be assigned at least N/k objects. E.g., there are N=280 students in this class. There are k=52 weeks in the year. Therefore, there must be at least 1 week during which at least 280/52= 5.38=6 students in the class have a birthday. CompSci 102 © Michael Frank

Then the total number of objects is at most Proof of G.P.P. By contradiction. Suppose every place has < N/k objects, thus ≤ N/k−1. Then the total number of objects is at most So, there are less than N objects, which contradicts our assumption of N objects! □ CompSci 102 © Michael Frank

G.P.P. Example Given: There are 280 students in the class. Without knowing anybody’s birthday, what is the largest value of n for which we can prove using the G.P.P. that at least n students must have been born in the same month? Answer: 280/12 = 23.3 = 24 CompSci 102 © Michael Frank

Permutations (§4.3) A permutation of a set S of objects is a sequence that contains each object in S exactly once. An ordered arrangement of r distinct elements of S is called an r-permutation of S. The number of r-permutations of a set with n=|S| elements is P(n,r) = n(n−1)…(n−r+1) = n!/(n−r)! CompSci 102 © Michael Frank

Permutation Example You are in a silly action movie where there is a bomb, and it is your job to disable it by cutting wires to the trigger device. There are 10 wires to the device. If you cut exactly the right three wires, in exactly the right order, you will disable the bomb, otherwise it will explode! If the wires all look the same, what are your chances of survival? P(10,3) = 10·9·8 = 720, so there is a 1 in 720 chance that you’ll survive! CompSci 102 © Michael Frank

Combinations (§4.3) An r-combination of elements of a set S is simply a subset TS with r members, |T|=r. The number of r-combinations of a set with n=|S| elements is Note that C(n,r) = C(n, n−r) Because choosing the r members of T is the same thing as choosing the n−r non-members of T. CompSci 102 © Michael Frank

Answer C(52,7) = P(52,7)/P(7,7) = 52·51·50·49·48·47·46 / 7·6·5·4·3·2·1 Combination Example How many distinct 7-card hands can be drawn from a standard 52-card deck? The order of cards in a hand doesn’t matter. Answer C(52,7) = P(52,7)/P(7,7) = 52·51·50·49·48·47·46 / 7·6·5·4·3·2·1 17 10 7 8 2 52·17·10·7·47·46 = 133,784,560 CompSci 102 © Michael Frank

Binomial coefficients Given (1 + x)r What are the coefficients of xr ? Show for (1 + x)4 CompSci 102 © Michael Frank

Important Theorems on Binomials Binomial thereom In the expansion of (1 + x)n, the coefficient of xr equals C(n,r) Pascal’s Identity Proofs? CompSci 102 © Michael Frank