Mathematical Preliminaries

Slides:



Advertisements
Similar presentations
Equivalence Relations
Advertisements

Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
The Engineering Design of Systems: Models and Methods
Week 5 - Friday.  What did we talk about last time?  Sequences  Summation and production notation  Proof by induction.
Discrete Mathematics Lecture 4 Harper Langston New York University.
Costas Busch - RPI1 Mathematical Preliminaries. Costas Busch - RPI2 Mathematical Preliminaries Sets Functions Relations Graphs Proof Techniques.
Courtesy Costas Busch - RPI1 Mathematical Preliminaries.
CSC 2300 Data Structures & Algorithms January 16, 2007 Chapter 1. Introduction.
Chapter 7 Functions Dr. Curry Guinn. Outline of Today Section 7.1: Functions Defined on General Sets Section 7.2: One-to-One and Onto Section 7.3: The.
1 CSCI 2400 section 3 Models of Computation Instructor: Costas Busch.
Discrete Mathematics, 1st Edition Kevin Ferland
2.4 Sequences and Summations
Mathematical Preliminaries Strings and Languages Preliminaries 1.
Week 15 - Wednesday.  What did we talk about last time?  Review first third of course.
CSC201 Analysis and Design of Algorithms Asst.Proof.Dr.Surasak Mungsing Oct-151 Lecture 2: Definition of algorithm and Mathematical.
Foundations of Discrete Mathematics Chapters 5 By Dr. Dalia M. Gil, Ph.D.
Chapter 2 Mathematical preliminaries 2.1 Set, Relation and Functions 2.2 Proof Methods 2.3 Logarithms 2.4 Floor and Ceiling Functions 2.5 Factorial and.
Mathematical Preliminaries. Sets Functions Relations Graphs Proof Techniques.
Discrete Mathematics and Its Applications Sixth Edition By Kenneth Rosen Copyright  The McGraw-Hill Companies, Inc. Permission required for reproduction.
Barnett/Ziegler/Byleen Finite Mathematics 11e1 Chapter 7 Review Important Terms, Symbols, Concepts 7.1. Logic A proposition is a statement (not a question.
Fall 2005Costas Busch - RPI1 Mathematical Preliminaries.
Prof. Busch - LSU1 Mathematical Preliminaries. Prof. Busch - LSU2 Mathematical Preliminaries Sets Functions Relations Graphs Proof Techniques.
Basic Structures: Sets, Functions, Sequences, and Sums CSC-2259 Discrete Structures Konstantin Busch - LSU1.
Mathematical Induction
Relations, Functions, and Countability
CompSci 102 Discrete Math for Computer Science
Mathematical Preliminaries
CS 103 Discrete Structures Lecture 13 Induction and Recursion (1)
Basic Structures: Sets, Functions, Sequences, and Sums.
Foundations of Discrete Mathematics Chapters 5 By Dr. Dalia M. Gil, Ph.D.
Sets Definition: A set is an unordered collection of objects, called elements or members of the set. A set is said to contain its elements. We write a.
Review 2 Basic Definitions Set - Collection of objects, usually denoted by capital letter Member, element - Object in a set, usually denoted by lower.
Mathematical Induction Section 5.1. Climbing an Infinite Ladder Suppose we have an infinite ladder: 1.We can reach the first rung of the ladder. 2.If.
1 Mathematical Preliminaries. 2 Sets Functions Relations Graphs Proof Techniques.
CS104:Discrete Structures Chapter 2: Proof Techniques.
CompSci 102 Discrete Math for Computer Science March 13, 2012 Prof. Rodger Slides modified from Rosen.
Set Theory Concepts Set – A collection of “elements” (objects, members) denoted by upper case letters A, B, etc. elements are lower case brackets are used.
Section 1.7. Definitions A theorem is a statement that can be shown to be true using: definitions other theorems axioms (statements which are given as.
Week 15 - Wednesday.  What did we talk about last time?  Review first third of course.
Chapter 2 1. Chapter Summary Sets (This Slide) The Language of Sets - Sec 2.1 – Lecture 8 Set Operations and Set Identities - Sec 2.2 – Lecture 9 Functions.
Week 8 - Wednesday.  What did we talk about last time?  Relations  Properties of relations  Reflexive  Symmetric  Transitive.
“It is impossible to define every concept.” For example a “set” can not be defined. But Here are a list of things we shall simply assume about sets. A.
Chapter 0 Discrete Mathematics 1. A collection of objects called elements; no repetition or order. A = {a 1, …, a n } or {a 1, a 2, …} N = {0, 1, 2, …}{1,
Chapter 5 1. Chapter Summary  Mathematical Induction  Strong Induction  Recursive Definitions  Structural Induction  Recursive Algorithms.
CSE 20: Discrete Mathematics for Computer Science Prof. Shachar Lovett.
Lecture 2: Proofs and Recursion. Lecture 2-1: Proof Techniques Proof methods : –Inductive reasoning Lecture 2-2 –Deductive reasoning Using counterexample.
Chapter 1 Logic and Proof.
Discrete Mathematics Lecture 6
Discrete Mathematics Lecture 3 (and 4)
Relations, Functions, and Matrices
Modeling with Recurrence Relations
CS 2210:0001 Discrete Structures Sets and Functions
Discrete Mathematics Lecture 7
Cartesian product Given two sets A, B we define their Cartesian product is the set of all the pairs whose first element is in A and second in B. Note that.
MATH 224 – Discrete Mathematics
Chapter 3 The Real Numbers.
Chapter 5 Induction and Recursion
Chapter 2 Sets and Functions.
Review 2.
Lecture 7 Functions.
CSE15 Discrete Mathematics 02/27/17
Lesson 5 Relations, mappings, countable and uncountable sets
COUNTING AND PROBABILITY
CSCI-2400 Models of Computation.
Lesson 5 Relations, mappings, countable and uncountable sets
Mathematical Preliminaries
Mathematical Background
Mathematical Induction
Agenda Proofs (Konsep Pembuktian) Direct Proofs & Counterexamples
Presentation transcript:

Mathematical Preliminaries Chapter 2 Mathematical Preliminaries

Sets The term set is used to prefer to any collection of objects, which are called members or elements of the set. A set is called finite if it contains n elements, for some constant n0, and infinite otherwise. An infinite set is called countable if its elements can be listed as the first element, second element, and so on; otherwise it is called uncountable. Cardinality, subset, union, intersection, difference, complement, disjoint, power set

Relations The Cartesian product: AB={(a,b)|aA, bB} A binary relation, or simply a relation, R from A to B is a set of ordered pairs (a,b) where aA and bB, that is, RAB. If A=B, R is a relation on the set A. Domain, range, symmetric/asymmetric/antisymmetric, reflexive/irreflexive, transitive, partial order A relation R on a set A is called an equivalence relation if it is reflexive, symmetric and transitive. In this case, R partitions A into equivalence classes C1, C2,..., Ck such that any two elements in one equivalence class are related by R.

Functions A function f is a (binary) relation such that for every element xDom(f) there is exactly one element yRan(f) with (x,y)f. In this case one usually writes f(x)=y instead of (x,y)f and says that y is the value or image of f at x. one to one, onto, bijection, one to one correspondence

Direct proof To prove that “PQ”, a direct proof works by assuming that P is true and then deducing the truth Q from the truth of P.

Indirect proof The implication “PQ” is logically equivalent to the contrapositive implication “QP”.

Proof by contradiction To prove that the statement “PQ” is true, we start by assuming that P is true but Q is false. If this assumption leads to a contradiction, it means that the assumption that “Q is false” must be wrong, and hence Q must follow from P.

Proof by counterexample When we are faced with a problem that requires proving or disproving a given assertion, we may start by trying to disprove the assertion with a counterexample. It is usually employed to show that a proposition that holds true quite often is not always true.

Mathematical induction First we prove that the property holds for n0. This is called the basis step. Then we prove that whenever the property is true for n0, n0+1, ... , n-1, then it must follow that the property is true for n. This is called the induction step. We then conclude that the property holds for all values of nn0.

A Puzzle for Pirates Ten pirates have got their hands on a hoard of 100 goldpieces, and wish to divide the loot between them. They are democratic pirates, in their own way, and it is their custom to make such divisions in the following manner. The fiercest pirate makes a proposal about the division, and everybody votes --- one vote each including the proposer. If 50% or more are in favour, the proposal passes and is implemented forthwith. Otherwise the proposer is thrown overboard and the procedure is repeated with the next fiercest pirate. All the pirates enjoy throwing people overboard, but given the choice they prefer hard cash. They dislike being thrown overboard themselves. All pirates are rational, know that the other pirates are rational, know that they know that... and so on. Moreover, no two pirates are equally fierce, so there is a precise 'pecking order' --- and it is known to them all. Finally: gold pieces are indivisible and arrangements to share pieces are not permitted (since no pirate trusts his fellows to stick to such an arrangement). It's every man for himself. Which proposal will maximize the fiercest pirate's gain? If the number of pirates is 20, 50,100,200,400,…, what is the result?

Logarithms

Floor and ceiling functions Let x be a real number. The floor of x, denoted by x, is defined as the greatest integer less than or equal to x. The ceiling of x, denoted by x, is defined as the least integer greater than or equal to x. x/2+x/2=x -x=-x, -x=-x Theorem: Let f(x) be a monotonically increasing function such that if f(x) is integer, then x is integer. Then f(x)=f(x) and f(x)=f(x)

Factorials 0!=1 n!=n(n-1)! if n1 Stirling’s formula:

Binomial coefficient

The pigeonhole principle Theorem 2.3 If n balls are distributed into m boxes, then 1) one box must contain at least n/m balls 2) one box must contain at most n/m balls

Summations

Approximation of summations by integration Let f(x) be a continuous function that is monotically decreasing Let f(x) be a continuous function that is monotically increasing

Recurrence relations A recursive formula is simply a formula that is defined in terms of itself. A recurrence relation is called linear homogeneous with constant coefficients if it is of the form f(n)=a1f(n-1)+a2f(n-2)+...+akf(n-k) In this case, f(n) is said to be of degree k. If an additional term involving a constant or a function of n appears in the recurrence, then it is called inhomogeneous.

Solution of recurrence relation Solution of linear homogeneous recurrences: characteristic equation Solution of inhomogeneous recurrences: some elementary inhomogeneous recurrences Solution of divide-and-conquer recurrences: 1) expanding the recurrence/Master Theorem 2) substitution 3) change of variables Some important results!

A Important Theorem Theorem 2.5: Let a and c be nonnegative integers, b, d and x nonnegative constants, and let n=ck, for some nonnegative integer k. Then the solution to the recurrence f(n)=af(n/c)+bnx is 1) If a<cx, then f(n)=(nx) 2) If a=cx, then f(n)=(nxlogn) 3) If a>cx, then f(n)=(n^logca)

Master Theorem Let a>=1, c>1 be constants, f(n) be a function, T(n) be defined on the negative integers by the recurrence T(n)=aT(n/c)+f(n) where we intepret n/c to mean either n/c or n/c. Then 1) If f(n)=O(n^(logca-)) for some >0, then T(n)=(n^logca) 2) If f(n)=(n^logca), then T(n)=((n^logca)logn) 3) If f(n)=(n^(logca+)) for some >0 and if a*f(n/c)kf(n) for some constant k<1 and all sufficiently large n, then T(n)=(f(n))