Computer Science Section 3.2

Size: px
Start display at page:

Download "Computer Science Section 3.2"

Transcription

1 Computer Science 180 Solutions for Recommended Exercises Section 3.. (a) If x > 4, then x > 16 x x. Also, if x > 17, then x > 17x x 17x 17x x. Seventeen is greater than four, so if x > 17, we have 0 < 17x x + x x + x = x 17x + 11 x. So, we let C = and k = 17. Accordingly, 17x + 11 is O ( x ). (b) If x > 3, then x > 104 x x x x + x 0 < x x x x. So, C = and k = 3 are our witnesses to the fact that x is O ( x ). (c) Using the Principle of Mathematical Induction (discussed in this course), we can prove that n < n for all positive integers n. Using calculus, we can prove that x < x for all real x. We can also use calculus to prove that logx (we assume that the base of this logarithm is ) is an increasing function, which essentially means that it preserves order. For now, we will assume that these results are true. Accordingly, for all x 1, 1 x < x log1 logx < log x 0 logx < x 0 xlogx < x xlogx < x xlogx 1 x. So, xlogx is O ( x ), with our witnesses being C = k = 1. 1

2 (d) Assume that x 4 / is O ( x ). Then, there exist real constants C and k such that for all x > k, x 4 / C x x C. Clearly, C must be positive. Now, let x 0 be the maximum of k +1 and C + 1. Then, x 0 > k, so by the statement above, x 0 C. On the other hand, x 0 C x0 C x0 > C 0 x 0 > C, which is a contradiction. Thus, x 4 / is not O ( x ). (e) Recall that x < x for all real x. Like in (e), we assume that x is O ( x ), so that we can find real constants C and k such that for all x > k, x C x x Cx. Of course, C must be positive. Now, let x 0 be the maximum of k + 1 and 7(C + 1). Then, x 0 > k, so by the statement above, x 0 Cx0. However, x 0 = ( x 0/3 ) 3 > (x0 /3) 3 = x 0 7 x 0 7(C + 1) 7 x 0 = (C + 1)x 0 > Cx 0 i.e. x 0 > Cx 0, which is a contradiction. Thus, x is not O ( x ). (f) x, known as the floor function, returns the largest integer n that is less than or equal to x i.e. n is the unique integer satisfying x 1 < n x n x x x. x, known as the ceiling function, returns the smallest integer n that is greater than or equal to x i.e. n is the unique integer satisfying Then, for all x 1, x n < x + 1 n < x + 1 x < x x x x (x + 1) = x + x x + x x = x, so x x x. Ergo, x x is O ( x ), with witnesses C = and k = 1.

3 6. For all x 1, 0 < x3 + x x + 1 so x 3 + x x + 1 < x3 + x x = x3 x + x x = 1 x x + x < x, x. Choosing C = and k = 1, we obtain the desired result. 8. (a) For all x 1, 0 < x + x 3 logx < x + x 3 (x) x x + x 4 = 3x 4 x + x 3 logx 3 x 4, so n = 4. (b) For all x 1, 0 < 3x 5 + (logx) 4 3x 5 + (x) 4 3x 5 + x 4 x = 4x 5 3x 5 + (logx) 4 4 x 5, so n = 5. (c) For all x 1, 0 < x4 + x + 1 x 3 x4 + x x + 1 x x x4 + x + 1 x x, so n = 1. = 3x4 x < 3x4 x 3 = 3x (d) For all x 1, 0 < x3 + 5logx x 4 < x3 + 5(x) + 1 x x3 + 5logx x , so n = 0. < x3 + 5x x 4 = x3 x 4 + 5x x 4 = 1 x + 5 x = x 1 0 < 1 x 0 < 1 x 3 x x 3 0 < x 3 x 4, so for all x > 1, x 3 1 x 4. Thus, x 3 is O ( x 4) with witnesses C = k = 1. Now assume that x 4 is O ( x 3), so that we can find real constants C and k such that for all x > k, x 4 C x 3 x C. Picking any x that is larger than both k and C yields a contradiction, so x 4 is not O ( x 3). 3

4 1. For all x 1, 0 logx < x 0 x logx x x 0 xlogx < x xlogx 1 x so xlogx is O ( x ) with witnesses C = k = 1. Now assume that x is O(xlogx), so that we can find real constants C and k such that for all x > k, x C xlogx x C logx. Again, C must be positive. Next, let x 0 be the maximum of k + 1 and 4C. Then, x 0 > k, so by our assumption, x 0 C logx 0. However, x 0 = > logx 0 logx 0 = = logx 0 4 (logx 0)/ logx 0 > log 4C 4 logx 0 = 4C 4 logx 0 = C logx 0 i.e. x 0 > C logx 0, which is a contradiction. Therefore, x is not O(xlogx). 14. (a) Assume that x 3 is O ( x ), so that we can find real constants C and k such that for all x > k, x 3 C x x C. Choosing any x that is greater than both k and C produces a contradiction, so the answer is no. (b) For all real x, x 3 1 x 3, so the answer is yes. (c) For all real x, x 3 x + x 3, and for all nonnegative x, x 3 x + x 3 = 1x + x 3, so the answer is yes. (d) For all x 1, 0 < x 3 x 4 x + x 4 x 3 1 x + x 4, so the answer is yes. (e) 3 x = log3 x = xlog3 = (xlog3)/3 3 xlog3 > 3 x 3 (f) For all real x, ( ) x, so the answer is yes. log3 x 3 1 x 3, so the answer is yes. 3 = ( ) log3 3 x 3 3 4

5 ( 0. (a) We use Theorems and 3: n 3 + n logn ) (logn + 1) + (17logn + 19) ( n 3 + ) = O (( n 3 + n logn ) (logn) + (17logn + 19) ( n 3 + )) = O (( n 3 + n logn ) (logn) + (17logn) ( n 3 + )) = O (( n 3 + n logn ) (logn) + (17logn) ( n 3)) = O ( n (n + logn)(logn) + (17logn) ( n 3)) = O ( n (n)(logn) + (17logn) ( n 3)) = O ( n 3 logn + 17n 3 logn ) = O ( n 3 logn ). (b) ( n + n )( n n) = O ( ( n ) ( n n)) = O(( n )(3 n )) = O(( 3) n ) = O(6 n ). (c) (n n + n n + 5 n )(n! + 5 n ) = O((n n + n n + 5 n )(n!)) = O((n n )(n!)) = O(n! n n ). 4. (a) We first show that 3x + 7 is O(x): for all x 7, 0 < 3x + 7 3x + x = 4x 3x x, so our witnesses are C = 4 and k = 7. Next, we show that 3x + 7 is Ω(x): for all x 1, 3x + 7 > 3x > 0 3x x, so our witnesses are C = 3 and k = 1. Since 3x + 7 is both O(x) and Ω(x), it is Θ(x), as required. (b) x + x 7 = ( x ) + (x 7) x + x 7 (Triangle Inequality) = x + (x) + ( 7) x + x + 7 (Triangle Inequality again) = x + x + 7 = x + x + 7 (provided that x is nonnegative) 5 (continued)

6 (continued) x + x + 7 (provided that x 1) x + x + x (provided that x 3) = 4x = 4 x. Accordingly, x + x 7 is O ( x ) with witnesses C = 4 and k = 3. Next, x + x 7 = x + (x 7) x (provided that x 7) 0 x + x 7 x, so x + x 7 is Ω ( x ) with witnesses C = and k = 7. Consequently, x + x 7 is Θ ( x ), as required. (c) x + 1 is the unique integer n satisfying ( x + 1 ) 1 < n x + 1 x 1 < x + 1 x + 1. Then, for all x 1, 0 < 1 x < 1 x + 1 (x 1) = x 1 < x + 1 x x, so x + 1 is Ω(x) with witnesses C = 1/ and k = 1. Also, for all x 1, 0 < x + 1 x + 1 < x + x = x x + 1 x, so x + 1 is O(x) with witnesses C = and k = 1. Ergo, x + 1 is Θ(x), as required. (d) For all x, x = x < x + 1 < x + x = x = ( ) x < (x)x = x. In other words, for all x, 1 < x < ( x + 1 ) 1/ < x log1 < logx < log ( x + 1 ) 1/ < logx 0 < logx < 1 log( x + 1 ) < logx (continued) 6

7 (continued) 0 < logx < log ( x + 1 ) < 4logx logx log ( x + 1 ) 4 logx. To show that log ( x + 1 ) is O(logx), we choose C = 4 and k =, and to show that it is Ω(logx), we make C = and k = ; both of these show that it is Θ(logx), as required. (e) For all positive x, x = log x log 10 x = log 10 log x log 10 x = (log x)log 10 log 10 x = (log 10 )log x log 10 x = (log 10 ) log x. Note that if a = b, then a b and a b are both true! Thus, log 10 x is Θ(log x) with witnesses C = log 10 and k = To prove this biconditional statement, we must prove each implication. First, assume that f (x) is O(g(x)). Then, there exist real constants C and k such that for all x > k, f (x) C g(x). Clearly, C must be posi...waitaminute! It could be zero! Precisely, C must be nonnegative. Say that C = 0. Then, for all real x, f (x) = 0, so g(x) 0 g(x) f (x) g(x) 1 f (x). Hence, g(x) is Ω( f (x)) with witnesses C = k = 1. Now say that C > 0. Then, for all x > k, f (x) C g(x) C g(x) f (x) g(x) 1 C f (x). Thus, g(x) is Ω( f (x)) with witnesses C = 1 C and k = k + 1 (this ensures that k > 0 and k > k). Either way, g(x) is Ω( f (x)). Next, assume that g(x) is Ω( f (x)). Then, there are positive numbers C and k such that for all x > k, g(x) C f (x) C f (x) g(x) f (x) 1 C g(x). Ergo, f (x) is O( f (x)) with witnesses C = 1 C and k = k. 7

Describe an algorithm for finding the smallest integer in a finite sequence of natural numbers.

Describe an algorithm for finding the smallest integer in a finite sequence of natural numbers. Homework #4 Problem One (2.1.2) Determine which characteristics o f an algorithm the following procedures have and which they lack. a) procedure double(n: positive integer) while n > 0 n := 2n Since n

More information

Announcements. CompSci 230 Discrete Math for Computer Science. The Growth of Functions. Section 3.2

Announcements. CompSci 230 Discrete Math for Computer Science. The Growth of Functions. Section 3.2 CompSci 230 Discrete Math for Computer Science Announcements Read Chap. 3.1-3.3 No recitation Friday, Oct 11 or Mon Oct 14 October 8, 2013 Prof. Rodger Section 3.2 Big-O Notation Big-O Estimates for Important

More information

The Growth of Functions (2A) Young Won Lim 4/6/18

The Growth of Functions (2A) Young Won Lim 4/6/18 Copyright (c) 2015-2018 Young W. Lim. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published

More information

3. Algorithms. What matters? How fast do we solve the problem? How much computer resource do we need?

3. Algorithms. What matters? How fast do we solve the problem? How much computer resource do we need? 3. Algorithms We will study algorithms to solve many different types of problems such as finding the largest of a sequence of numbers sorting a sequence of numbers finding the shortest path between two

More information

Growth of Functions. As an example for an estimate of computation time, let us consider the sequential search algorithm.

Growth of Functions. As an example for an estimate of computation time, let us consider the sequential search algorithm. Function Growth of Functions Subjects to be Learned Contents big oh max function big omega big theta little oh little omega Introduction One of the important criteria in evaluating algorithms is the time

More information

5.6 Logarithmic and Exponential Equations

5.6 Logarithmic and Exponential Equations SECTION 5.6 Logarithmic and Exponential Equations 305 5.6 Logarithmic and Exponential Equations PREPARING FOR THIS SECTION Before getting started, review the following: Solving Equations Using a Graphing

More information

On the number of ways of writing t as a product of factorials

On the number of ways of writing t as a product of factorials On the number of ways of writing t as a product of factorials Daniel M. Kane December 3, 005 Abstract Let N 0 denote the set of non-negative integers. In this paper we prove that lim sup n, m N 0 : n!m!

More information

UNIT 3 INTEGRATION 3.0 INTRODUCTION 3.1 OBJECTIVES. Structure

UNIT 3 INTEGRATION 3.0 INTRODUCTION 3.1 OBJECTIVES. Structure Calculus UNIT 3 INTEGRATION Structure 3.0 Introduction 3.1 Objectives 3.2 Basic Integration Rules 3.3 Integration by Substitution 3.4 Integration of Rational Functions 3.5 Integration by Parts 3.6 Answers

More information

Homework #2 Solutions Due: September 5, for all n N n 3 = n2 (n + 1) 2 4

Homework #2 Solutions Due: September 5, for all n N n 3 = n2 (n + 1) 2 4 Do the following exercises from the text: Chapter (Section 3):, 1, 17(a)-(b), 3 Prove that 1 3 + 3 + + n 3 n (n + 1) for all n N Proof The proof is by induction on n For n N, let S(n) be the statement

More information

Exam in Discrete Mathematics

Exam in Discrete Mathematics Exam in Discrete Mathematics First Year at the Faculty of Engineering and Science and the Technical Faculty of IT and Design June 4th, 018, 9.00-1.00 This exam consists of 11 numbered pages with 14 problems.

More information

Computer Science Section 1.6

Computer Science Section 1.6 Computer Science 180 Solutions for Recommended Exercises Section 1.6. Let m and n be any two even integers (possibly the same). Then, there exist integers k and l such that m = k and n = l. Consequently,

More information

Mathematics for Business and Economics - I. Chapter 5. Functions (Lecture 9)

Mathematics for Business and Economics - I. Chapter 5. Functions (Lecture 9) Mathematics for Business and Economics - I Chapter 5. Functions (Lecture 9) Functions The idea of a function is this: a correspondence between two sets D and R such that to each element of the first set,

More information

Review 1. Andreas Klappenecker

Review 1. Andreas Klappenecker Review 1 Andreas Klappenecker Summary Propositional Logic, Chapter 1 Predicate Logic, Chapter 1 Proofs, Chapter 1 Sets, Chapter 2 Functions, Chapter 2 Sequences and Sums, Chapter 2 Asymptotic Notations,

More information

CmSc 250 Intro to Algorithms. Mathematical Review. 1. Basic Algebra. (a + b) 2 = a 2 + 2ab + b 2 (a - b) 2 = a 2-2ab + b 2 a 2 - b 2 = (a + b)(a - b)

CmSc 250 Intro to Algorithms. Mathematical Review. 1. Basic Algebra. (a + b) 2 = a 2 + 2ab + b 2 (a - b) 2 = a 2-2ab + b 2 a 2 - b 2 = (a + b)(a - b) CmSc 250 Intro to Algorithms Mathematical Review 1. Basic Algebra (a + b) 2 = a 2 + 2ab + b 2 (a - b) 2 = a 2-2ab + b 2 a 2 - b 2 = (a + b)(a - b) a/x + b/y = (ay + bx)/xy 2. Exponents X n = XXX..X, n

More information

Functions and Their Graphs

Functions and Their Graphs Functions and Their Graphs DEFINITION Function A function from a set D to a set Y is a rule that assigns a unique (single) element ƒ(x) Y to each element x D. A symbolic way to say y is a function of x

More information

We want to show P (n) is true for all integers

We want to show P (n) is true for all integers Generalized Induction Proof: Let P (n) be the proposition 1 + 2 + 2 2 + + 2 n = 2 n+1 1. We want to show P (n) is true for all integers n 0. Generalized Induction Example: Use generalized induction to

More information

CISC 235: Topic 1. Complexity of Iterative Algorithms

CISC 235: Topic 1. Complexity of Iterative Algorithms CISC 235: Topic 1 Complexity of Iterative Algorithms Outline Complexity Basics Big-Oh Notation Big-Ω and Big-θ Notation Summations Limitations of Big-Oh Analysis 2 Complexity Complexity is the study of

More information

Big O Notation. P. Danziger

Big O Notation. P. Danziger 1 Comparing Algorithms We have seen that in many cases we would like to compare two algorithms. Generally, the efficiency of an algorithm can be guaged by how long it takes to run as a function of the

More information

Skill 6 Exponential and Logarithmic Functions

Skill 6 Exponential and Logarithmic Functions Skill 6 Exponential and Logarithmic Functions Skill 6a: Graphs of Exponential Functions Skill 6b: Solving Exponential Equations (not requiring logarithms) Skill 6c: Definition of Logarithms Skill 6d: Graphs

More information

4. Convex optimization problems (part 1: general)

4. Convex optimization problems (part 1: general) EE/AA 578, Univ of Washington, Fall 2016 4. Convex optimization problems (part 1: general) optimization problem in standard form convex optimization problems quasiconvex optimization 4 1 Optimization problem

More information

Algorithms and Their Complexity

Algorithms and Their Complexity CSCE 222 Discrete Structures for Computing David Kebo Houngninou Algorithms and Their Complexity Chapter 3 Algorithm An algorithm is a finite sequence of steps that solves a problem. Computational complexity

More information

Properties of the Integers

Properties of the Integers Properties of the Integers The set of all integers is the set and the subset of Z given by Z = {, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, }, N = {0, 1, 2, 3, 4, }, is the set of nonnegative integers (also called

More information

a + b = b + a and a b = b a. (a + b) + c = a + (b + c) and (a b) c = a (b c). a (b + c) = a b + a c and (a + b) c = a c + b c.

a + b = b + a and a b = b a. (a + b) + c = a + (b + c) and (a b) c = a (b c). a (b + c) = a b + a c and (a + b) c = a c + b c. Properties of the Integers The set of all integers is the set and the subset of Z given by Z = {, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, }, N = {0, 1, 2, 3, 4, }, is the set of nonnegative integers (also called

More information

Data structures Exercise 1 solution. Question 1. Let s start by writing all the functions in big O notation:

Data structures Exercise 1 solution. Question 1. Let s start by writing all the functions in big O notation: Data structures Exercise 1 solution Question 1 Let s start by writing all the functions in big O notation: f 1 (n) = 2017 = O(1), f 2 (n) = 2 log 2 n = O(n 2 ), f 3 (n) = 2 n = O(2 n ), f 4 (n) = 1 = O

More information

Notes on the second moment method, Erdős multiplication tables

Notes on the second moment method, Erdős multiplication tables Notes on the second moment method, Erdős multiplication tables January 25, 20 Erdős multiplication table theorem Suppose we form the N N multiplication table, containing all the N 2 products ab, where

More information

Big O Notation. P. Danziger

Big O Notation. P. Danziger 1 Comparing Algorithms We have seen that in many cases we would like to compare two algorithms. Generally, the efficiency of an algorithm can be guaged by how long it takes to run as a function of the

More information

Analytic Number Theory Solutions

Analytic Number Theory Solutions Analytic Number Theory Solutions Sean Li Cornell University sxl6@cornell.edu Jan. 03 Introduction This document is a work-in-progress solution manual for Tom Apostol s Introduction to Analytic Number Theory.

More information

Static Problem Set 2 Solutions

Static Problem Set 2 Solutions Static Problem Set Solutions Jonathan Kreamer July, 0 Question (i) Let g, h be two concave functions. Is f = g + h a concave function? Prove it. Yes. Proof: Consider any two points x, x and α [0, ]. Let

More information

Announcements. CompSci 102 Discrete Math for Computer Science. Chap. 3.1 Algorithms. Specifying Algorithms

Announcements. CompSci 102 Discrete Math for Computer Science. Chap. 3.1 Algorithms. Specifying Algorithms CompSci 102 Discrete Math for Computer Science Announcements Read for next time Chap. 3.1-3.3 Homework 3 due Tuesday We ll finish Chapter 2 first today February 7, 2012 Prof. Rodger Chap. 3.1 Algorithms

More information

d. What are the steps for finding the y intercepts algebraically?(hint: what is equal to 0?)

d. What are the steps for finding the y intercepts algebraically?(hint: what is equal to 0?) st Semester Pre Calculus Exam Review You will not receive hints on your exam. Make certain you know how to answer each of the following questions. This is a test grade. Your WORK and EXPLANATIONS are graded

More information

Chapter 1. Pigeonhole Principle

Chapter 1. Pigeonhole Principle Chapter 1. Pigeonhole Principle Prof. Tesler Math 184A Fall 2017 Prof. Tesler Ch. 1. Pigeonhole Principle Math 184A / Fall 2017 1 / 16 Pigeonhole principle https://commons.wikimedia.org/wiki/file:toomanypigeons.jpg

More information

Taylor and Maclaurin Series

Taylor and Maclaurin Series Taylor and Maclaurin Series MATH 211, Calculus II J. Robert Buchanan Department of Mathematics Spring 2018 Background We have seen that some power series converge. When they do, we can think of them as

More information

4.1 Real-valued functions of a real variable

4.1 Real-valued functions of a real variable Chapter 4 Functions When introducing relations from a set A to a set B we drew an analogy with co-ordinates in the x-y plane. Instead of coming from R, the first component of an ordered pair comes from

More information

Reading 10 : Asymptotic Analysis

Reading 10 : Asymptotic Analysis CS/Math 240: Introduction to Discrete Mathematics Fall 201 Instructor: Beck Hasti and Gautam Prakriya Reading 10 : Asymptotic Analysis In the last reading, we analyzed the running times of various algorithms.

More information

MATH 324 Summer 2011 Elementary Number Theory. Notes on Mathematical Induction. Recall the following axiom for the set of integers.

MATH 324 Summer 2011 Elementary Number Theory. Notes on Mathematical Induction. Recall the following axiom for the set of integers. MATH 4 Summer 011 Elementary Number Theory Notes on Mathematical Induction Principle of Mathematical Induction Recall the following axiom for the set of integers. Well-Ordering Axiom for the Integers If

More information

CMSC Discrete Mathematics SOLUTIONS TO SECOND MIDTERM EXAM November, 2005

CMSC Discrete Mathematics SOLUTIONS TO SECOND MIDTERM EXAM November, 2005 CMSC-37110 Discrete Mathematics SOLUTIONS TO SECOND MIDTERM EXAM November, 2005 Instructor: László Babai Ryerson 164 e-mail: laci@cs This exam contributes 20% to your course grade. 1. (6 points) Let a

More information

Mean Value Theorem. MATH 161 Calculus I. J. Robert Buchanan. Summer Department of Mathematics

Mean Value Theorem. MATH 161 Calculus I. J. Robert Buchanan. Summer Department of Mathematics Mean Value Theorem MATH 161 Calculus I J. Robert Buchanan Department of Mathematics Summer 2018 Background: Corollary to the Intermediate Value Theorem Corollary Suppose f is continuous on the closed interval

More information

Performance and Scalability. Lars Karlsson

Performance and Scalability. Lars Karlsson Performance and Scalability Lars Karlsson Outline Complexity analysis Runtime, speedup, efficiency Amdahl s Law and scalability Cost and overhead Cost optimality Iso-efficiency function Case study: matrix

More information

Solutions. Problem 1: Suppose a polynomial in n of degree d has the form

Solutions. Problem 1: Suppose a polynomial in n of degree d has the form Assignment 1 1. Problem 3-1 on p. 57 2. Problem 3-2 on p. 58 3. Problem 4-5 on p. 86 4. Problem 6-1 on p. 142 5. Problem 7-4 on p. 162 6. Prove correctness (including halting) of SelectionSort (use loop

More information

Mean Value Theorem. MATH 161 Calculus I. J. Robert Buchanan. Summer Department of Mathematics

Mean Value Theorem. MATH 161 Calculus I. J. Robert Buchanan. Summer Department of Mathematics Mean Value Theorem MATH 161 Calculus I J. Robert Buchanan Department of Mathematics Summer 2018 Background: Corollary to the Intermediate Value Theorem Corollary Suppose f is continuous on the closed interval

More information

number j, we define the multinomial coefficient by If x = (x 1,, x n ) is an n-tuple of real numbers, we set

number j, we define the multinomial coefficient by If x = (x 1,, x n ) is an n-tuple of real numbers, we set . Taylor s Theorem in R n Definition.. An n-dimensional multi-index is an n-tuple of nonnegative integer = (,, n. The absolute value of is defined ( to be = + + n. For each natural j number j, we define

More information

8. Conjugate functions

8. Conjugate functions L. Vandenberghe EE236C (Spring 2013-14) 8. Conjugate functions closed functions conjugate function 8-1 Closed set a set C is closed if it contains its boundary: x k C, x k x = x C operations that preserve

More information

CHAPTER 3 REVIEW QUESTIONS MATH 3034 Spring a 1 b 1

CHAPTER 3 REVIEW QUESTIONS MATH 3034 Spring a 1 b 1 . Let U = { A M (R) A = and b 6 }. CHAPTER 3 REVIEW QUESTIONS MATH 334 Spring 7 a b a and b are integers and a 6 (a) Let S = { A U det A = }. List the elements of S; that is S = {... }. (b) Let T = { A

More information

Solutions Manual. Selected odd-numbers problems from. Chapter 3. Proof: Introduction to Higher Mathematics. Seventh Edition

Solutions Manual. Selected odd-numbers problems from. Chapter 3. Proof: Introduction to Higher Mathematics. Seventh Edition Solutions Manual Selected odd-numbers problems from Chapter 3 of Proof: Introduction to Higher Mathematics Seventh Edition Warren W. Esty and Norah C. Esty 5 4 3 2 1 2 Section 3.1. Inequalities Chapter

More information

Reexam in Discrete Mathematics

Reexam in Discrete Mathematics Reexam in Discrete Mathematics First Year at the Faculty of Engineering and Science and the Technical Faculty of IT and Design August 15th, 2017, 9.00-13.00 This exam consists of 11 numbered pages with

More information

Complete Induction and the Well- Ordering Principle

Complete Induction and the Well- Ordering Principle Complete Induction and the Well- Ordering Principle Complete Induction as a Rule of Inference In mathematical proofs, complete induction (PCI) is a rule of inference of the form P (a) P (a + 1) P (b) k

More information

Local Fields. Chapter Absolute Values and Discrete Valuations Definitions and Comments

Local Fields. Chapter Absolute Values and Discrete Valuations Definitions and Comments Chapter 9 Local Fields The definition of global field varies in the literature, but all definitions include our primary source of examples, number fields. The other fields that are of interest in algebraic

More information

Problems for Putnam Training

Problems for Putnam Training Problems for Putnam Training 1 Number theory Problem 1.1. Prove that for each positive integer n, the number is not prime. 10 1010n + 10 10n + 10 n 1 Problem 1.2. Show that for any positive integer n,

More information

Discrete Mathematics CS October 17, 2006

Discrete Mathematics CS October 17, 2006 Discrete Mathematics CS 2610 October 17, 2006 Uncountable sets Theorem: The set of real numbers is uncountable. If a subset of a set is uncountable, then the set is uncountable. The cardinality of a subset

More information

Recitation 7: Existence Proofs and Mathematical Induction

Recitation 7: Existence Proofs and Mathematical Induction Math 299 Recitation 7: Existence Proofs and Mathematical Induction Existence proofs: To prove a statement of the form x S, P (x), we give either a constructive or a non-contructive proof. In a constructive

More information

Homework 5 Solutions

Homework 5 Solutions Homework 5 Solutions ECS 0 (Fall 17) Patrice Koehl koehl@cs.ucdavis.edu ovember 1, 017 Exercise 1 a) Show that the following statement is true: If there exists a real number x such that x 4 +1 = 0, then

More information

8. Limit Laws. lim(f g)(x) = lim f(x) lim g(x), (x) = lim x a f(x) g lim x a g(x)

8. Limit Laws. lim(f g)(x) = lim f(x) lim g(x), (x) = lim x a f(x) g lim x a g(x) 8. Limit Laws 8.1. Basic Limit Laws. If f and g are two functions and we know the it of each of them at a given point a, then we can easily compute the it at a of their sum, difference, product, constant

More information

Division Algorithm B1 Introduction to the Division Algorithm (Procedure) quotient remainder

Division Algorithm B1 Introduction to the Division Algorithm (Procedure) quotient remainder A Survey of Divisibility Page 1 SECTION B Division Algorithm By the end of this section you will be able to apply the division algorithm or procedure Our aim in this section is to show that for any given

More information

a. See the textbook for examples of proving logical equivalence using truth tables. b. There is a real number x for which f (x) < 0. (x 1) 2 > 0.

a. See the textbook for examples of proving logical equivalence using truth tables. b. There is a real number x for which f (x) < 0. (x 1) 2 > 0. For some problems, several sample proofs are given here. Problem 1. a. See the textbook for examples of proving logical equivalence using truth tables. b. There is a real number x for which f (x) < 0.

More information

6.4 Logarithmic Equations and Inequalities

6.4 Logarithmic Equations and Inequalities 6.4 Logarithmic Equations and Inequalities 459 6.4 Logarithmic Equations and Inequalities In Section 6.3 we solved equations and inequalities involving exponential functions using one of two basic strategies.

More information

Structure of R. Chapter Algebraic and Order Properties of R

Structure of R. Chapter Algebraic and Order Properties of R Chapter Structure of R We will re-assemble calculus by first making assumptions about the real numbers. All subsequent results will be rigorously derived from these assumptions. Most of the assumptions

More information

Divide and Conquer. Arash Rafiey. 27 October, 2016

Divide and Conquer. Arash Rafiey. 27 October, 2016 27 October, 2016 Divide the problem into a number of subproblems Divide the problem into a number of subproblems Conquer the subproblems by solving them recursively or if they are small, there must be

More information

ECOM Discrete Mathematics

ECOM Discrete Mathematics ECOM 2311- Discrete Mathematics Chapter # 3 : Algorithms Fall, 2013/2014 ECOM 2311- Discrete Mathematics - Ch.3 Dr. Musbah Shaat 1 / 41 Outline 1 Algorithms 2 The Growth of Functions 3 Complexity of Algorithms

More information

MTH4100 Calculus I. Lecture notes for Week 2. Thomas Calculus, Sections 1.3 to 1.5. Rainer Klages

MTH4100 Calculus I. Lecture notes for Week 2. Thomas Calculus, Sections 1.3 to 1.5. Rainer Klages MTH4100 Calculus I Lecture notes for Week 2 Thomas Calculus, Sections 1.3 to 1.5 Rainer Klages School of Mathematical Sciences Queen Mary University of London Autumn 2009 Reading Assignment: read Thomas

More information

1. Given the public RSA encryption key (e, n) = (5, 35), find the corresponding decryption key (d, n).

1. Given the public RSA encryption key (e, n) = (5, 35), find the corresponding decryption key (d, n). MATH 135: Randomized Exam Practice Problems These are the warm-up exercises and recommended problems taken from all the extra practice sets presented in random order. The challenge problems have not been

More information

Thus f is continuous at x 0. Matthew Straughn Math 402 Homework 6

Thus f is continuous at x 0. Matthew Straughn Math 402 Homework 6 Matthew Straughn Math 402 Homework 6 Homework 6 (p. 452) 14.3.3, 14.3.4, 14.3.5, 14.3.8 (p. 455) 14.4.3* (p. 458) 14.5.3 (p. 460) 14.6.1 (p. 472) 14.7.2* Lemma 1. If (f (n) ) converges uniformly to some

More information

Well Ordered Sets (continued)

Well Ordered Sets (continued) Well Ordered Sets (continued) Theorem 8 Given any two well-ordered sets, either they are isomorphic, or one is isomorphic to an initial segment of the other. Proof Let a,< and b, be well-ordered sets.

More information

Math 324 Summer 2012 Elementary Number Theory Notes on Mathematical Induction

Math 324 Summer 2012 Elementary Number Theory Notes on Mathematical Induction Math 4 Summer 01 Elementary Number Theory Notes on Mathematical Induction Principle of Mathematical Induction Recall the following axiom for the set of integers. Well-Ordering Axiom for the Integers If

More information

CSCE 222 Discrete Structures for Computing. Review for Exam 1. Dr. Hyunyoung Lee !!!

CSCE 222 Discrete Structures for Computing. Review for Exam 1. Dr. Hyunyoung Lee !!! CSCE 222 Discrete Structures for Computing Review for Exam 1 Dr. Hyunyoung Lee 1 Topics Propositional Logic (Sections 1.1, 1.2 and 1.3) Predicate Logic (Sections 1.4 and 1.5) Rules of Inferences and Proofs

More information

Lesson 29 MA Nick Egbert

Lesson 29 MA Nick Egbert Lesson 9 MA 16 Nick Egbert Overview In this lesson we build on the previous two b complicating our domains of integration and discussing the average value of functions of two variables. Lesson So far the

More information

Algorithms 2/6/2018. Algorithms. Enough Mathematical Appetizers! Algorithm Examples. Algorithms. Algorithm Examples. Algorithm Examples

Algorithms 2/6/2018. Algorithms. Enough Mathematical Appetizers! Algorithm Examples. Algorithms. Algorithm Examples. Algorithm Examples Enough Mathematical Appetizers! Algorithms What is an algorithm? Let us look at something more interesting: Algorithms An algorithm is a finite set of precise instructions for performing a computation

More information

(2) Generalize De Morgan s laws for n sets and prove the laws by induction. 1

(2) Generalize De Morgan s laws for n sets and prove the laws by induction. 1 ARS DIGITA UNIVERSITY MONTH 2: DISCRETE MATHEMATICS PROFESSOR SHAI SIMONSON PROBLEM SET 2 SOLUTIONS SET, FUNCTIONS, BIG-O, RATES OF GROWTH (1) Prove by formal logic: (a) The complement of the union of

More information

Homework 5 Solutions

Homework 5 Solutions Homework 5 Solutions ECS 0 (Fall 17) Patrice Koehl koehl@cs.ucdavis.edu February 8, 019 Exercise 1 a) Show that the following statement is true: If there exist two integers n and m such that n + n + 1

More information

Chapter 3 Exercise #20 on p. 82

Chapter 3 Exercise #20 on p. 82 19 views Chapter 3 Exercise #20 on p. 82 You may start your answer as follows. ε Let ε > 0. Since { p n } is Cauchy, there exists N 1 such that d ( p n, p m ) < for n, m N. 2 Remark. In coming up with

More information

Fast Convolution; Strassen s Method

Fast Convolution; Strassen s Method Fast Convolution; Strassen s Method 1 Fast Convolution reduction to subquadratic time polynomial evaluation at complex roots of unity interpolation via evaluation at complex roots of unity 2 The Master

More information

Integer-Valued Polynomials

Integer-Valued Polynomials Integer-Valued Polynomials LA Math Circle High School II Dillon Zhi October 11, 2015 1 Introduction Some polynomials take integer values p(x) for all integers x. The obvious examples are the ones where

More information

Section 4.4 Functions. CS 130 Discrete Structures

Section 4.4 Functions. CS 130 Discrete Structures Section 4.4 Functions CS 130 Discrete Structures Function Definitions Let S and T be sets. A function f from S to T, f: S T, is a subset of S x T where each member of S appears exactly once as the first

More information

Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics

Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights

More information

1 What is numerical analysis and scientific computing?

1 What is numerical analysis and scientific computing? Mathematical preliminaries 1 What is numerical analysis and scientific computing? Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations)

More information

The Growth of Functions and Big-O Notation

The Growth of Functions and Big-O Notation The Growth of Functions and Big-O Notation Big-O Notation Big-O notation allows us to describe the aymptotic growth of a function without concern for i) constant multiplicative factors, and ii) lower-order

More information

EE 546, Univ of Washington, Spring Proximal mapping. introduction. review of conjugate functions. proximal mapping. Proximal mapping 6 1

EE 546, Univ of Washington, Spring Proximal mapping. introduction. review of conjugate functions. proximal mapping. Proximal mapping 6 1 EE 546, Univ of Washington, Spring 2012 6. Proximal mapping introduction review of conjugate functions proximal mapping Proximal mapping 6 1 Proximal mapping the proximal mapping (prox-operator) of a convex

More information

MS 2001: Test 1 B Solutions

MS 2001: Test 1 B Solutions MS 2001: Test 1 B Solutions Name: Student Number: Answer all questions. Marks may be lost if necessary work is not clearly shown. Remarks by me in italics and would not be required in a test - J.P. Question

More information

MSc Mas6002, Introductory Material Mathematical Methods Exercises

MSc Mas6002, Introductory Material Mathematical Methods Exercises MSc Mas62, Introductory Material Mathematical Methods Exercises These exercises are on a range of mathematical methods which are useful in different parts of the MSc, especially calculus and linear algebra.

More information

CSE 373: Data Structures and Algorithms Pep Talk; Algorithm Analysis. Riley Porter Winter 2017

CSE 373: Data Structures and Algorithms Pep Talk; Algorithm Analysis. Riley Porter Winter 2017 CSE 373: Data Structures and Algorithms Pep Talk; Algorithm Analysis Riley Porter Announcements Op4onal Java Review Sec4on: PAA A102 Tuesday, January 10 th, 3:30-4:30pm. Any materials covered will be posted

More information

Standard forms for writing numbers

Standard forms for writing numbers Standard forms for writing numbers In order to relate the abstract mathematical descriptions of familiar number systems to the everyday descriptions of numbers by decimal expansions and similar means,

More information

(x k ) sequence in F, lim x k = x x F. If F : R n R is a function, level sets and sublevel sets of F are any sets of the form (respectively);

(x k ) sequence in F, lim x k = x x F. If F : R n R is a function, level sets and sublevel sets of F are any sets of the form (respectively); STABILITY OF EQUILIBRIA AND LIAPUNOV FUNCTIONS. By topological properties in general we mean qualitative geometric properties (of subsets of R n or of functions in R n ), that is, those that don t depend

More information

PRINTABLE VERSION. Practice Final. Question 1. Find the coordinates of the y-intercept for 5x 9y + 6 = 0. 2 (0, ) 3 3 (0, ) 2 2 (0, ) 3 6 (0, ) 5

PRINTABLE VERSION. Practice Final. Question 1. Find the coordinates of the y-intercept for 5x 9y + 6 = 0. 2 (0, ) 3 3 (0, ) 2 2 (0, ) 3 6 (0, ) 5 PRINTABLE VERSION Practice Final Question Find the coordinates of the y-intercept for 5x 9y + 6 = 0. (0, ) (0, ) (0, ) 6 (0, ) 5 6 (0, ) 5 Question Find the slope of the line: 7x 4y = 0 7 4 4 4 7 7 4 4

More information

Solutions for Math 225 Assignment #4 1

Solutions for Math 225 Assignment #4 1 Solutions for Math 225 Assignment #4 () Let B {(3, 4), (4, 5)} and C {(, ), (0, )} be two ordered bases of R 2 (a) Find the change-of-basis matrices P C B and P B C (b) Find v] B if v] C ] (c) Find v]

More information

CALCULUS II - Self Test

CALCULUS II - Self Test 175 2- CALCULUS II - Self Test Instructor: Andrés E. Caicedo November 9, 2009 Name These questions are divided into four groups. Ideally, you would answer YES to all questions in group A, to most questions

More information

Domain and range of exponential and logarithmic function *

Domain and range of exponential and logarithmic function * OpenStax-CNX module: m15461 1 Domain and range of exponential and logarithmic function * Sunil Kumar Singh This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License

More information

4.4 Recurrences and Big-O

4.4 Recurrences and Big-O 4.4. RECURRENCES AND BIG-O 91 4.4 Recurrences and Big-O In this section, we will extend our knowledge of recurrences, showing how to solve recurrences with big-o s in them, recurrences in which n b k,

More information

Solutions to Homework 2

Solutions to Homework 2 Solutions to Homewor Due Tuesday, July 6,. Chapter. Problem solution. If the series for ln+z and ln z both converge, +z then we can find the series for ln z by term-by-term subtraction of the two series:

More information

Physical Chemistry - Problem Drill 02: Math Review for Physical Chemistry

Physical Chemistry - Problem Drill 02: Math Review for Physical Chemistry Physical Chemistry - Problem Drill 02: Math Review for Physical Chemistry No. 1 of 10 1. The Common Logarithm is based on the powers of 10. Solve the logarithmic equation: log(x+2) log(x-1) = 1 (A) 1 (B)

More information

Warm Up Lesson Presentation Lesson Quiz. Holt McDougal Algebra 2

Warm Up Lesson Presentation Lesson Quiz. Holt McDougal Algebra 2 4-5 Warm Up Lesson Presentation Lesson Quiz Algebra 2 Warm Up Solve. 1. log 16 x = 3 2 64 2. log x 1.331 = 3 1.1 3. log10,000 = x 4 Objectives Solve exponential and logarithmic equations and equalities.

More information

Four Basic Sets. Divisors

Four Basic Sets. Divisors Four Basic Sets Z = the integers Q = the rationals R = the real numbers C = the complex numbers Divisors Definition. Suppose a 0 and b = ax, where a, b, and x are integers. Then we say a divides b (or

More information

Multivariate distributions

Multivariate distributions CHAPTER Multivariate distributions.. Introduction We want to discuss collections of random variables (X, X,..., X n ), which are known as random vectors. In the discrete case, we can define the density

More information

Discrete dynamics on the real line

Discrete dynamics on the real line Chapter 2 Discrete dynamics on the real line We consider the discrete time dynamical system x n+1 = f(x n ) for a continuous map f : R R. Definitions The forward orbit of x 0 is: O + (x 0 ) = {x 0, f(x

More information

Dr. Abdulla Eid. Section 3.8 Derivative of the inverse function and logarithms 3 Lecture. Dr. Abdulla Eid. MATHS 101: Calculus I. College of Science

Dr. Abdulla Eid. Section 3.8 Derivative of the inverse function and logarithms 3 Lecture. Dr. Abdulla Eid. MATHS 101: Calculus I. College of Science Section 3.8 Derivative of the inverse function and logarithms 3 Lecture College of Science MATHS 101: Calculus I (University of Bahrain) Logarithmic Differentiation 1 / 19 Topics 1 Inverse Functions (1

More information

Well-Ordering Principle. Axiom: Every nonempty subset of Z + has a least element. That is, if S Z + and S, then S has a smallest element.

Well-Ordering Principle. Axiom: Every nonempty subset of Z + has a least element. That is, if S Z + and S, then S has a smallest element. Well-Ordering Principle Axiom: Every nonempty subset of Z + has a least element. That is, if S Z + and S, then S has a smallest element. Well-Ordering Principle Example: Use well-ordering property to prove

More information

10.1 Radical Expressions and Functions Math 51 Professor Busken

10.1 Radical Expressions and Functions Math 51 Professor Busken 0. Radical Expressions and Functions Math 5 Professor Busken Objectives Find square roots without a calculator Simplify expressions of the form n a n Evaluate radical functions and find the domain of radical

More information

MATH 104A HW 02 SOLUTION KEY

MATH 104A HW 02 SOLUTION KEY .3 Algorithms and Convergence Solutions by Jon Lo Kim Lin - Fall 04 MATH 04A HW 0 SOLUTION KEY You are encouraged to collaborate with your classmates and utilize internet resources provided you adhere

More information

State Precalculus/Trigonometry Contest 2008

State Precalculus/Trigonometry Contest 2008 State Precalculus/Trigonometry Contest 008 Select the best answer for each of the following questions and mark it on the answer sheet provided. Be sure to read all the answer choices before making your

More information

IS 709/809: Computational Methods in IS Research Fall Exam Review

IS 709/809: Computational Methods in IS Research Fall Exam Review IS 709/809: Computational Methods in IS Research Fall 2017 Exam Review Nirmalya Roy Department of Information Systems University of Maryland Baltimore County www.umbc.edu Exam When: Tuesday (11/28) 7:10pm

More information

1 The distributive law

1 The distributive law THINGS TO KNOW BEFORE GOING INTO DISCRETE MATHEMATICS The distributive law The distributive law is this: a(b + c) = ab + bc This can be generalized to any number of terms between parenthesis; for instance:

More information

Solutions for Chapter Solutions for Chapter 17. Section 17.1 Exercises

Solutions for Chapter Solutions for Chapter 17. Section 17.1 Exercises Solutions for Chapter 17 403 17.6 Solutions for Chapter 17 Section 17.1 Exercises 1. Suppose A = {0,1,2,3,4}, B = {2,3,4,5} and f = {(0,3),(1,3),(2,4),(3,2),(4,2)}. State the domain and range of f. Find

More information