Problem Set 1 Solutions

Size: px
Start display at page:

Download "Problem Set 1 Solutions"

Transcription

1 V R N N N R f ] R S Itroductio to Algorithms September 12, 2003 Massachusetts Istitute of echology 6046J/18410J rofessors Shafi Goldwasser ad Silvio Micali Hadout 7 roblem Set 1 Solutios roblem 1-1 Recurrece Relatios Solve the followig recurreces Give a boud for each problem If you are uable to fid a boud, provide as tight upper ( or ) ad lower ( or ) bouds as you ca fid Justify your aswers You may assume that (a)! #"%$'&() - By part 1 of the Master Method (b) *,+-!!/1032 /4032* - By part 3 of the Master Method (c) 6, 6 87: ; < Substitute = for he resultig relatio is =>?@ BA C;= Clearly, this is => ad thus 6 (d) *D EF G H ( ) ( ) 2 NN I G %J K M M M M M M M M M M M MM R R R R 16 2 R S S S S S S S x S x16 S x2 S S S S S S 16x 16x16 16x2 2x 2x16 2x2 he figure above shows the recursio tree that helps us i guessig a solutio We have that U #"%$XW BY \^] a3bdc e \^] a3bdc g ïhj ( H gk l hus we guess that lm * ad we prove it by substitutio Assume that =>UDop = for a appropriate costat o ad for all =rqs he we have that

2 v ( \ 2 Hadout 7: roblem Set 1 Solutios t v ou sou a sou 3w a3b ox, c {z o yh a3b o h c o a-b ~} for o {z Hece, * the other had by ispectio we have g ƒ % * Hece l % (e) D hk{ * ˆ 87:? j (f) *D /1032s lš * (g) /40323 :* - By ispectio ad substitutio G k*œ & Œ /4032* - By part 2 of the Master Method roblem 1-2 Asymptotic Notatio Rak the followig fuctios by order of growth; that is, fid a arragemet }' }pžpžpž }' of the H fuctios satisfyig,,,, E k H artitio your list ito equivalece classes such that x ad are i the same class if ad oly if x* *' (he fuctio /40-2 is discussed o pages -6 of CRS) G 4 # & z ]%]%]%] z /4032! #"%$8 #"%$ /4032* #"%$ š/4032,{b /1032dC œ he followig are ordered asymptotically from smallest to largest, are as follows (two fuctios, ad are o the same lie if x *X ): ]%]%]%] z

3 Hadout 7: roblem Set 1 Solutios 3 /4032 š/4032 /4032d ž G 1 # & /40-2 #"%$ #"%$3 #"%$ z roblem 1-3 Sieve of Eratosthees DB he Sieve of Eratosthees, iveted circa 200 BC, is a algorithm to fid all prime umbers betwee 2 ad a iput umber Ÿ he algorithm works as follows: we begi with a list of all itegers from 2 to Ÿ For each = Ÿ, we cross out (ie mark as composite) each multiple of = (> = ) that is less tha or equal to Ÿ Whe this process termiates, oly the prime umbers betwee 2 ad Ÿ are umarked

4 4 Hadout 7: roblem Set 1 Solutios Below, we give pseudocode for this algorithm At the begiig of the algorithm, every etry i the array is iitialized to true, ie is true for all } ˆŸ At the ed of the algorithm, is true iff is prime ERASHENES-SIEVE Ÿ : 1 et for all from 2 to Ÿ!ª «2 for = to Ÿ : 3 4 while y p= kÿ : l # {= `±6²! 6 D 7 edwhile 8 edfor he table below shows the values of the array elemets ³ŽpŽpŽXŸ (lie 7) durig a executio of the algorithm ru o iput Ÿ µ{z at the ed of each while loop i #¹ º #» º ¼ ½pº #¾ º # º ¼ ÁÀBº # º #à º ¼ œä)åæº œä{ä'º œä¹ º œä» º 1 2 F F F F F 3 F F F F F F 4 F F F F F F F F F F F F 13 F F F F F F rove that the ERASHENES-SIEVE algorithm is correct; that is, prove that upo termiatio, is true iff is prime Hit: You ca use the followig pre-coditio ad post-coditio ad you ca prove the suggested loop ivariat for the for loop (lie 2) et Ç is true iff is prime Ç Ç re-coditio: ŸÈ, ost-coditio: }ŠÉ such that ~ oop Ivariat: } É such that DŸ D= represet the statemet: l 6 We will use the give loop-ivariat for the for loop (lie 2) ad the relevat pre- ad postcoditios to prove the correctess of the algorithm We will ow prove the give loop ivariat Namely, we will prove by iductio that whe = is assiged the value (lie 2), Ç holds for all } Whe = is assiged the value 2, Ê is true sice it was iitialized to be true hus, we have proved that before we execute the for loop the first time, the base case of the loop ivariat holds

5 Hadout 7: roblem Set 1 Solutios Now we will assume that whe = use this iductive hypothesis to prove that whe = })~, is assiged the value, Ç holds for all }g is assiged the value µ, Ç We will holds for all et s cosider a executio of the while loop with =r Sice goes from to ËŸ =Ì, y = is always at least Sice oly elemets with ˆy {= are (re)set to false, o elemet l 6 6 with will chage value hus, Ç for }, will hold at the ed of the executio of the while loop It remais to show that Ç holds at the ed of the executio of the while loop If Í is true, the it caot have ay divisor Î such that ÏÎ the it does ot have a divisor ÎÐ ivariat is correct So k must be prime If k is prime, hus, it must be true hus, we have proved that the loop Whe =mƒÿ, o the fial executio of the for loop, we have that Ç due to the correctess of the loop ivariat holds for all, ƒÿ,

Matriculation number: You have 90 minutes to complete the exam of InformatikIIb. The following rules apply:

Matriculation number: You have 90 minutes to complete the exam of InformatikIIb. The following rules apply: Departmet of Iformatics Prof. Dr. Michael Böhle Bizmühlestrasse 14 8050 Zurich Phoe: +41 44 635 4333 Email: boehle@ifi.uzh.ch AlgoDat Midterm1 Sprig 016 08.04.016 Name: Matriculatio umber: Advice You have

More information

CSI 2101 Discrete Structures Winter Homework Assignment #4 (100 points, weight 5%) Due: Thursday, April 5, at 1:00pm (in lecture)

CSI 2101 Discrete Structures Winter Homework Assignment #4 (100 points, weight 5%) Due: Thursday, April 5, at 1:00pm (in lecture) CSI 101 Discrete Structures Witer 01 Prof. Lucia Moura Uiversity of Ottawa Homework Assigmet #4 (100 poits, weight %) Due: Thursday, April, at 1:00pm (i lecture) Program verificatio, Recurrece Relatios

More information

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018 CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical

More information

Model of Computation and Runtime Analysis

Model of Computation and Runtime Analysis Model of Computatio ad Rutime Aalysis Model of Computatio Model of Computatio Specifies Set of operatios Cost of operatios (ot ecessarily time) Examples Turig Machie Radom Access Machie (RAM) PRAM Map

More information

CS / MCS 401 Homework 3 grader solutions

CS / MCS 401 Homework 3 grader solutions CS / MCS 401 Homework 3 grader solutios assigmet due July 6, 016 writte by Jāis Lazovskis maximum poits: 33 Some questios from CLRS. Questios marked with a asterisk were ot graded. 1 Use the defiitio of

More information

Department of Informatics Prof. Dr. Michael Böhlen Binzmühlestrasse Zurich Phone:

Department of Informatics Prof. Dr. Michael Böhlen Binzmühlestrasse Zurich Phone: Departmet of Iformatics Prof. Dr. Michael Böhle Bizmühlestrasse 14 8050 Zurich Phoe: +41 44 635 4333 Email: boehle@ifi.uzh.ch Iformatik II Midterm1 Sprig 018 3.03.018 Advice You have 90 miutes to complete

More information

Data Structures and Algorithm. Xiaoqing Zheng

Data Structures and Algorithm. Xiaoqing Zheng Data Structures ad Algorithm Xiaoqig Zheg zhegxq@fudaeduc What are algorithms? A sequece of computatioal steps that trasform the iput ito the output Sortig problem: Iput: A sequece of umbers

More information

Test One (Answer Key)

Test One (Answer Key) CS395/Ma395 (Sprig 2005) Test Oe Name: Page 1 Test Oe (Aswer Key) CS395/Ma395: Aalysis of Algorithms This is a closed book, closed otes, 70 miute examiatio. It is worth 100 poits. There are twelve (12)

More information

CS 270 Algorithms. Oliver Kullmann. Growth of Functions. Divide-and- Conquer Min-Max- Problem. Tutorial. Reading from CLRS for week 2

CS 270 Algorithms. Oliver Kullmann. Growth of Functions. Divide-and- Conquer Min-Max- Problem. Tutorial. Reading from CLRS for week 2 Geeral remarks Week 2 1 Divide ad First we cosider a importat tool for the aalysis of algorithms: Big-Oh. The we itroduce a importat algorithmic paradigm:. We coclude by presetig ad aalysig two examples.

More information

Recurrence Relations

Recurrence Relations Recurrece Relatios Aalysis of recursive algorithms, such as: it factorial (it ) { if (==0) retur ; else retur ( * factorial(-)); } Let t be the umber of multiplicatios eeded to calculate factorial(). The

More information

This Lecture. Divide and Conquer. Merge Sort: Algorithm. Merge Sort Algorithm. MergeSort (Example) - 1. MergeSort (Example) - 2

This Lecture. Divide and Conquer. Merge Sort: Algorithm. Merge Sort Algorithm. MergeSort (Example) - 1. MergeSort (Example) - 2 This Lecture Divide-ad-coquer techique for algorithm desig. Example the merge sort. Writig ad solvig recurreces Divide ad Coquer Divide-ad-coquer method for algorithm desig: Divide: If the iput size is

More information

A recurrence equation is just a recursive function definition. It defines a function at one input in terms of its value on smaller inputs.

A recurrence equation is just a recursive function definition. It defines a function at one input in terms of its value on smaller inputs. CS23 Algorithms Hadout #6 Prof Ly Turbak September 8, 200 Wellesley College RECURRENCES This hadout summarizes highlights of CLRS Chapter 4 ad Appedix A (CLR Chapters 3 & 4) Two-Step Strategy for Aalyzig

More information

CS583 Lecture 02. Jana Kosecka. some materials here are based on E. Demaine, D. Luebke slides

CS583 Lecture 02. Jana Kosecka. some materials here are based on E. Demaine, D. Luebke slides CS583 Lecture 02 Jaa Kosecka some materials here are based o E. Demaie, D. Luebke slides Previously Sample algorithms Exact ruig time, pseudo-code Approximate ruig time Worst case aalysis Best case aalysis

More information

CS:3330 (Prof. Pemmaraju ): Assignment #1 Solutions. (b) For n = 3, we will have 3 men and 3 women with preferences as follows: m 1 : w 3 > w 1 > w 2

CS:3330 (Prof. Pemmaraju ): Assignment #1 Solutions. (b) For n = 3, we will have 3 men and 3 women with preferences as follows: m 1 : w 3 > w 1 > w 2 Shiyao Wag CS:3330 (Prof. Pemmaraju ): Assigmet #1 Solutios Problem 1 (a) Cosider iput with me m 1, m,..., m ad wome w 1, w,..., w with the followig prefereces: All me have the same prefereces for wome:

More information

CSE 1400 Applied Discrete Mathematics Number Theory and Proofs

CSE 1400 Applied Discrete Mathematics Number Theory and Proofs CSE 1400 Applied Discrete Mathematics Number Theory ad Proofs Departmet of Computer Scieces College of Egieerig Florida Tech Sprig 01 Problems for Number Theory Backgroud Number theory is the brach of

More information

Fundamental Algorithms

Fundamental Algorithms Fudametal Algorithms Chapter 2b: Recurreces Michael Bader Witer 2014/15 Chapter 2b: Recurreces, Witer 2014/15 1 Recurreces Defiitio A recurrece is a (i-equality that defies (or characterizes a fuctio i

More information

1. By using truth tables prove that, for all statements P and Q, the statement

1. By using truth tables prove that, for all statements P and Q, the statement Author: Satiago Salazar Problems I: Mathematical Statemets ad Proofs. By usig truth tables prove that, for all statemets P ad Q, the statemet P Q ad its cotrapositive ot Q (ot P) are equivalet. I example.2.3

More information

CS 332: Algorithms. Quicksort

CS 332: Algorithms. Quicksort CS 33: Aorithms Quicsort David Luebe //03 Homewor Assiged today, due ext Wedesday Will be o web page shortly after class Go over ow David Luebe //03 Review: Quicsort Sorts i place Sorts O( ) i the average

More information

and each factor on the right is clearly greater than 1. which is a contradiction, so n must be prime.

and each factor on the right is clearly greater than 1. which is a contradiction, so n must be prime. MATH 324 Summer 200 Elemetary Number Theory Solutios to Assigmet 2 Due: Wedesday July 2, 200 Questio [p 74 #6] Show that o iteger of the form 3 + is a prime, other tha 2 = 3 + Solutio: If 3 + is a prime,

More information

Data Structures Lecture 9

Data Structures Lecture 9 Fall 2017 Fag Yu Software Security Lab. Dept. Maagemet Iformatio Systems, Natioal Chegchi Uiversity Data Structures Lecture 9 Midterm o Dec. 7 (9:10-12:00am, 106) Lec 1-9, TextBook Ch1-8, 11,12 How to

More information

Continued Fractions and Pell s Equation

Continued Fractions and Pell s Equation Max Lah Joatha Spiegel May, 06 Abstract Cotiued fractios provide a useful, ad arguably more atural, way to uderstad ad represet real umbers as a alterative to decimal expasios I this paper, we eumerate

More information

Signals and Systems. Problem Set: From Continuous-Time to Discrete-Time

Signals and Systems. Problem Set: From Continuous-Time to Discrete-Time Sigals ad Systems Problem Set: From Cotiuous-Time to Discrete-Time Updated: October 5, 2017 Problem Set Problem 1 - Liearity ad Time-Ivariace Cosider the followig systems ad determie whether liearity ad

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

PAPER : IIT-JAM 2010

PAPER : IIT-JAM 2010 MATHEMATICS-MA (CODE A) Q.-Q.5: Oly oe optio is correct for each questio. Each questio carries (+6) marks for correct aswer ad ( ) marks for icorrect aswer.. Which of the followig coditios does NOT esure

More information

Algorithms and Data Structures Lecture IV

Algorithms and Data Structures Lecture IV Algorithms ad Data Structures Lecture IV Simoas Šalteis Aalborg Uiversity simas@cs.auc.dk September 5, 00 1 This Lecture Aalyzig the ruig time of recursive algorithms (such as divide-ad-coquer) Writig

More information

Classification of problem & problem solving strategies. classification of time complexities (linear, logarithmic etc)

Classification of problem & problem solving strategies. classification of time complexities (linear, logarithmic etc) Classificatio of problem & problem solvig strategies classificatio of time complexities (liear, arithmic etc) Problem subdivisio Divide ad Coquer strategy. Asymptotic otatios, lower boud ad upper boud:

More information

Exam 2 CMSC 203 Fall 2009 Name SOLUTION KEY Show All Work! 1. (16 points) Circle T if the corresponding statement is True or F if it is False.

Exam 2 CMSC 203 Fall 2009 Name SOLUTION KEY Show All Work! 1. (16 points) Circle T if the corresponding statement is True or F if it is False. 1 (1 poits) Circle T if the correspodig statemet is True or F if it is False T F For ay positive iteger,, GCD(, 1) = 1 T F Every positive iteger is either prime or composite T F If a b mod p, the (a/p)

More information

PROBLEM SET 5 SOLUTIONS 126 = , 37 = , 15 = , 7 = 7 1.

PROBLEM SET 5 SOLUTIONS 126 = , 37 = , 15 = , 7 = 7 1. Math 7 Sprig 06 PROBLEM SET 5 SOLUTIONS Notatios. Give a real umber x, we will defie sequeces (a k ), (x k ), (p k ), (q k ) as i lecture.. (a) (5 pts) Fid the simple cotiued fractio represetatios of 6

More information

Solutions to Math 347 Practice Problems for the final

Solutions to Math 347 Practice Problems for the final Solutios to Math 347 Practice Problems for the fial 1) True or False: a) There exist itegers x,y such that 50x + 76y = 6. True: the gcd of 50 ad 76 is, ad 6 is a multiple of. b) The ifiimum of a set is

More information

Math 140A Elementary Analysis Homework Questions 1

Math 140A Elementary Analysis Homework Questions 1 Math 14A Elemetary Aalysis Homewor Questios 1 1 Itroductio 1.1 The Set N of Natural Numbers 1 Prove that 1 2 2 2 2 1 ( 1(2 1 for all atural umbers. 2 Prove that 3 11 (8 5 4 2 for all N. 4 (a Guess a formula

More information

Bertrand s Postulate. Theorem (Bertrand s Postulate): For every positive integer n, there is a prime p satisfying n < p 2n.

Bertrand s Postulate. Theorem (Bertrand s Postulate): For every positive integer n, there is a prime p satisfying n < p 2n. Bertrad s Postulate Our goal is to prove the followig Theorem Bertrad s Postulate: For every positive iteger, there is a prime p satisfyig < p We remark that Bertrad s Postulate is true by ispectio for,,

More information

MATH 324 Summer 2006 Elementary Number Theory Solutions to Assignment 2 Due: Thursday July 27, 2006

MATH 324 Summer 2006 Elementary Number Theory Solutions to Assignment 2 Due: Thursday July 27, 2006 MATH 34 Summer 006 Elemetary Number Theory Solutios to Assigmet Due: Thursday July 7, 006 Departmet of Mathematical ad Statistical Scieces Uiversity of Alberta Questio [p 74 #6] Show that o iteger of the

More information

Chapter 4. Fourier Series

Chapter 4. Fourier Series Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,

More information

Different kinds of Mathematical Induction

Different kinds of Mathematical Induction Differet ids of Mathematical Iductio () Mathematical Iductio Give A N, [ A (a A a A)] A N () (First) Priciple of Mathematical Iductio Let P() be a propositio (ope setece), if we put A { : N p() is true}

More information

Math F215: Induction April 7, 2013

Math F215: Induction April 7, 2013 Math F25: Iductio April 7, 203 Iductio is used to prove that a collectio of statemets P(k) depedig o k N are all true. A statemet is simply a mathematical phrase that must be either true or false. Here

More information

Read carefully the instructions on the answer book and make sure that the particulars required are entered on each answer book.

Read carefully the instructions on the answer book and make sure that the particulars required are entered on each answer book. THE UNIVERSITY OF WARWICK FIRST YEAR EXAMINATION: Jauary 2009 Aalysis I Time Allowed:.5 hours Read carefully the istructios o the aswer book ad make sure that the particulars required are etered o each

More information

4.3 Growth Rates of Solutions to Recurrences

4.3 Growth Rates of Solutions to Recurrences 4.3. GROWTH RATES OF SOLUTIONS TO RECURRENCES 81 4.3 Growth Rates of Solutios to Recurreces 4.3.1 Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer.

More information

Sums, products and sequences

Sums, products and sequences Sums, products ad sequeces How to write log sums, e.g., 1+2+ (-1)+ cocisely? i=1 Sum otatio ( sum from 1 to ): i 3 = 1 + 2 + + If =3, i=1 i = 1+2+3=6. The ame ii does ot matter. Could use aother letter

More information

MATH 304: MIDTERM EXAM SOLUTIONS

MATH 304: MIDTERM EXAM SOLUTIONS MATH 304: MIDTERM EXAM SOLUTIONS [The problems are each worth five poits, except for problem 8, which is worth 8 poits. Thus there are 43 possible poits.] 1. Use the Euclidea algorithm to fid the greatest

More information

Model of Computation and Runtime Analysis

Model of Computation and Runtime Analysis Model of Computatio ad Rutime Aalysis Model of Computatio Model of Computatio Specifies Set of operatios Cost of operatios (ot ecessarily time) Examples Turig Machie Radom Access Machie (RAM) PRAM Map

More information

Math 4400/6400 Homework #7 solutions

Math 4400/6400 Homework #7 solutions MATH 4400 problems. Math 4400/6400 Homewor #7 solutios 1. Let p be a prime umber. Show that the order of 1 + p modulo p 2 is exactly p. Hit: Expad (1 + p) p by the biomial theorem, ad recall from MATH

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

PUTNAM TRAINING PROBABILITY

PUTNAM TRAINING PROBABILITY PUTNAM TRAINING PROBABILITY (Last udated: December, 207) Remark. This is a list of exercises o robability. Miguel A. Lerma Exercises. Prove that the umber of subsets of {, 2,..., } with odd cardiality

More information

CSE 4095/5095 Topics in Big Data Analytics Spring 2017; Homework 1 Solutions

CSE 4095/5095 Topics in Big Data Analytics Spring 2017; Homework 1 Solutions CSE 09/09 Topics i ig Data Aalytics Sprig 2017; Homework 1 Solutios Note: Solutios to problems,, ad 6 are due to Marius Nicolae. 1. Cosider the followig algorithm: for i := 1 to α log e do Pick a radom

More information

End-of-Year Contest. ERHS Math Club. May 5, 2009

End-of-Year Contest. ERHS Math Club. May 5, 2009 Ed-of-Year Cotest ERHS Math Club May 5, 009 Problem 1: There are 9 cois. Oe is fake ad weighs a little less tha the others. Fid the fake coi by weighigs. Solutio: Separate the 9 cois ito 3 groups (A, B,

More information

CSI 5163 (95.573) ALGORITHM ANALYSIS AND DESIGN

CSI 5163 (95.573) ALGORITHM ANALYSIS AND DESIGN CSI 5163 (95.573) ALGORITHM ANALYSIS AND DESIGN CSI 5163 (95.5703) ALGORITHM ANALYSIS AND DESIGN (3 cr.) (T) Topics of curret iterest i the desig ad aalysis of computer algorithms for graphtheoretical

More information

SOLVED EXAMPLES

SOLVED EXAMPLES Prelimiaries Chapter PELIMINAIES Cocept of Divisibility: A o-zero iteger t is said to be a divisor of a iteger s if there is a iteger u such that s tu I this case we write t s (i) 6 as ca be writte as

More information

Curve Sketching Handout #5 Topic Interpretation Rational Functions

Curve Sketching Handout #5 Topic Interpretation Rational Functions Curve Sketchig Hadout #5 Topic Iterpretatio Ratioal Fuctios A ratioal fuctio is a fuctio f that is a quotiet of two polyomials. I other words, p ( ) ( ) f is a ratioal fuctio if p ( ) ad q ( ) are polyomials

More information

Linear Programming and the Simplex Method

Linear Programming and the Simplex Method Liear Programmig ad the Simplex ethod Abstract This article is a itroductio to Liear Programmig ad usig Simplex method for solvig LP problems i primal form. What is Liear Programmig? Liear Programmig is

More information

Analysis of Algorithms. Introduction. Contents

Analysis of Algorithms. Introduction. Contents Itroductio The focus of this module is mathematical aspects of algorithms. Our mai focus is aalysis of algorithms, which meas evaluatig efficiecy of algorithms by aalytical ad mathematical methods. We

More information

(b) What is the probability that a particle reaches the upper boundary n before the lower boundary m?

(b) What is the probability that a particle reaches the upper boundary n before the lower boundary m? MATH 529 The Boudary Problem The drukard s walk (or boudary problem) is oe of the most famous problems i the theory of radom walks. Oe versio of the problem is described as follows: Suppose a particle

More information

Recursive Algorithms. Recurrences. Recursive Algorithms Analysis

Recursive Algorithms. Recurrences. Recursive Algorithms Analysis Recursive Algorithms Recurreces Computer Sciece & Egieerig 35: Discrete Mathematics Christopher M Bourke cbourke@cseuledu A recursive algorithm is oe i which objects are defied i terms of other objects

More information

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ.

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ. 2 5. Weighted umber of late jobs 5.1. Release dates ad due dates: maximimizig the weight of o-time jobs Oce we add release dates, miimizig the umber of late jobs becomes a sigificatly harder problem. For

More information

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) =

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) = AN INTRODUCTION TO SCHRÖDER AND UNKNOWN NUMBERS NICK DUFRESNE Abstract. I this article we will itroduce two types of lattice paths, Schröder paths ad Ukow paths. We will examie differet properties of each,

More information

Chapter Vectors

Chapter Vectors Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it

More information

Dynamic Programming. Sequence Of Decisions

Dynamic Programming. Sequence Of Decisions Dyamic Programmig Sequece of decisios. Problem state. Priciple of optimality. Dyamic Programmig Recurrece Equatios. Solutio of recurrece equatios. Sequece Of Decisios As i the greedy method, the solutio

More information

Dynamic Programming. Sequence Of Decisions. 0/1 Knapsack Problem. Sequence Of Decisions

Dynamic Programming. Sequence Of Decisions. 0/1 Knapsack Problem. Sequence Of Decisions Dyamic Programmig Sequece Of Decisios Sequece of decisios. Problem state. Priciple of optimality. Dyamic Programmig Recurrece Equatios. Solutio of recurrece equatios. As i the greedy method, the solutio

More information

Zeros of Polynomials

Zeros of Polynomials Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree

More information

ITEC 360 Data Structures and Analysis of Algorithms Spring for n 1

ITEC 360 Data Structures and Analysis of Algorithms Spring for n 1 ITEC 360 Data Structures ad Aalysis of Algorithms Sprig 006 1. Prove that f () = 60 + 5 + 1 is Θ ( ). 60 + 5 + 1 60 + 5 + = 66 for 1 Take C 1 = 66 f () = 60 + 5 + 1 is O( ) Sice 60 + 5 + 1 60 for 1 If

More information

CS 5150/6150: Assignment 1 Due: Sep 23, 2010

CS 5150/6150: Assignment 1 Due: Sep 23, 2010 CS 5150/6150: Assigmet 1 Due: Sep 23, 2010 Wei Liu September 24, 2010 Q1: (1) Usig master theorem: a = 7, b = 4, f() = O(). Because f() = log b a ε holds whe ε = log b a = log 4 7, we ca apply the first

More information

MATH301 Real Analysis (2008 Fall) Tutorial Note #7. k=1 f k (x) converges pointwise to S(x) on E if and

MATH301 Real Analysis (2008 Fall) Tutorial Note #7. k=1 f k (x) converges pointwise to S(x) on E if and MATH01 Real Aalysis (2008 Fall) Tutorial Note #7 Sequece ad Series of fuctio 1: Poitwise Covergece ad Uiform Covergece Part I: Poitwise Covergece Defiitio of poitwise covergece: A sequece of fuctios f

More information

SEQUENCE AND SERIES NCERT

SEQUENCE AND SERIES NCERT 9. Overview By a sequece, we mea a arragemet of umbers i a defiite order accordig to some rule. We deote the terms of a sequece by a, a,..., etc., the subscript deotes the positio of the term. I view of

More information

These Two Weeks. Mathematical Induction Chapter 4 Lecture 10 & Lecture 13. CPRE 310 Discrete Mathematics. Mathematical Induction Problems

These Two Weeks. Mathematical Induction Chapter 4 Lecture 10 & Lecture 13. CPRE 310 Discrete Mathematics. Mathematical Induction Problems These Two Wees CPRE 0 Discrete Mathematics Mathematical Iductio Chapter Lecture 0 & Lecture Lecture 0 Mathematical Iductio (MI) MI Problems Lecture, Review Lecture February 9, Test I A list of topics ad

More information

Topic 1 2: Sequences and Series. A sequence is an ordered list of numbers, e.g. 1, 2, 4, 8, 16, or

Topic 1 2: Sequences and Series. A sequence is an ordered list of numbers, e.g. 1, 2, 4, 8, 16, or Topic : Sequeces ad Series A sequece is a ordered list of umbers, e.g.,,, 8, 6, or,,,.... A series is a sum of the terms of a sequece, e.g. + + + 8 + 6 + or... Sigma Notatio b The otatio f ( k) is shorthad

More information

A B = φ No conclusion. 2. (5) List the values of the sets below. Let A = {n 2 : n P n 5} = {1,4,9,16,25} and B = {n 4 : n P n 5} = {1,16,81,256,625}

A B = φ No conclusion. 2. (5) List the values of the sets below. Let A = {n 2 : n P n 5} = {1,4,9,16,25} and B = {n 4 : n P n 5} = {1,16,81,256,625} CPSC 070 Aswer Keys Test # October 1, 014 1. (a) (5) Defie (A B) to be those elemets i set A but ot i set B. Use set membership tables to determie what elemets are cotaied i (A (B A)). Use set membership

More information

+ au n+1 + bu n = 0.)

+ au n+1 + bu n = 0.) Lecture 6 Recurreces - kth order: u +k + a u +k +... a k u k 0 where a... a k are give costats, u 0... u k are startig coditios. (Simple case: u + au + + bu 0.) How to solve explicitly - first, write characteristic

More information

Math 113 Exam 3 Practice

Math 113 Exam 3 Practice Math Exam Practice Exam will cover.-.9. This sheet has three sectios. The first sectio will remid you about techiques ad formulas that you should kow. The secod gives a umber of practice questios for you

More information

Chapter 2. Asymptotic Notation

Chapter 2. Asymptotic Notation Asyptotic Notatio 3 Chapter Asyptotic Notatio Goal : To siplify the aalysis of ruig tie by gettig rid of details which ay be affected by specific ipleetatio ad hardware. [1] The Big Oh (O-Notatio) : It

More information

MATHEMATICS. 61. The differential equation representing the family of curves where c is a positive parameter, is of

MATHEMATICS. 61. The differential equation representing the family of curves where c is a positive parameter, is of MATHEMATICS 6 The differetial equatio represetig the family of curves where c is a positive parameter, is of Order Order Degree (d) Degree (a,c) Give curve is y c ( c) Differetiate wrt, y c c y Hece differetial

More information

MA541 : Real Analysis. Tutorial and Practice Problems - 1 Hints and Solutions

MA541 : Real Analysis. Tutorial and Practice Problems - 1 Hints and Solutions MA54 : Real Aalysis Tutorial ad Practice Problems - Hits ad Solutios. Suppose that S is a oempty subset of real umbers that is bouded (i.e. bouded above as well as below). Prove that if S sup S. What ca

More information

Introduction to Computational Biology Homework 2 Solution

Introduction to Computational Biology Homework 2 Solution Itroductio to Computatioal Biology Homework 2 Solutio Problem 1: Cocave gap pealty fuctio Let γ be a gap pealty fuctio defied over o-egative itegers. The fuctio γ is called sub-additive iff it satisfies

More information

Please do NOT write in this box. Multiple Choice. Total

Please do NOT write in this box. Multiple Choice. Total Istructor: Math 0560, Worksheet Alteratig Series Jauary, 3000 For realistic exam practice solve these problems without lookig at your book ad without usig a calculator. Multiple choice questios should

More information

The Binomial Theorem

The Binomial Theorem The Biomial Theorem Lecture 47 Sectio 9.7 Robb T. Koether Hampde-Sydey College Thu, Apr 8, 03 Robb T. Koether (Hampde-Sydey College The Biomial Theorem Thu, Apr 8, 03 / 7 Combiatios Pascal s Triagle 3

More information

COMP285 Midterm Exam Department of Mathematics

COMP285 Midterm Exam Department of Mathematics COMP85 Midterm Exam Departmet of Mathematics Fall 010/011 - November 8, 010 Name: Studet Number: Please fiish withi 90 miutes. All poits above 100 are cosidered as bous poit. You ca reach maximal 1 poits.

More information

Objective Mathematics

Objective Mathematics 6. If si () + cos () =, the is equal to :. If <

More information

ECE4270 Fundamentals of DSP. Lecture 2 Discrete-Time Signals and Systems & Difference Equations. Overview of Lecture 2. More Discrete-Time Systems

ECE4270 Fundamentals of DSP. Lecture 2 Discrete-Time Signals and Systems & Difference Equations. Overview of Lecture 2. More Discrete-Time Systems ECE4270 Fudametals of DSP Lecture 2 Discrete-Time Sigals ad Systems & Differece Equatios School of ECE Ceter for Sigal ad Iformatio Processig Georgia Istitute of Techology Overview of Lecture 2 Aoucemet

More information

3.2 Properties of Division 3.3 Zeros of Polynomials 3.4 Complex and Rational Zeros of Polynomials

3.2 Properties of Division 3.3 Zeros of Polynomials 3.4 Complex and Rational Zeros of Polynomials Math 60 www.timetodare.com 3. Properties of Divisio 3.3 Zeros of Polyomials 3.4 Complex ad Ratioal Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered

More information

Complex Analysis Spring 2001 Homework I Solution

Complex Analysis Spring 2001 Homework I Solution Complex Aalysis Sprig 2001 Homework I Solutio 1. Coway, Chapter 1, sectio 3, problem 3. Describe the set of poits satisfyig the equatio z a z + a = 2c, where c > 0 ad a R. To begi, we see from the triagle

More information

PRACTICE FINAL/STUDY GUIDE SOLUTIONS

PRACTICE FINAL/STUDY GUIDE SOLUTIONS Last edited December 9, 03 at 4:33pm) Feel free to sed me ay feedback, icludig commets, typos, ad mathematical errors Problem Give the precise meaig of the followig statemets i) a f) L ii) a + f) L iii)

More information

Disjoint set (Union-Find)

Disjoint set (Union-Find) CS124 Lecture 7 Fall 2018 Disjoit set (Uio-Fid) For Kruskal s algorithm for the miimum spaig tree problem, we foud that we eeded a data structure for maitaiig a collectio of disjoit sets. That is, we eed

More information

CS161: Algorithm Design and Analysis Handout #10 Stanford University Wednesday, 10 February 2016

CS161: Algorithm Design and Analysis Handout #10 Stanford University Wednesday, 10 February 2016 CS161: Algorithm Desig ad Aalysis Hadout #10 Staford Uiversity Wedesday, 10 February 2016 Lecture #11: Wedesday, 10 February 2016 Topics: Example midterm problems ad solutios from a log time ago Sprig

More information

sin(n) + 2 cos(2n) n 3/2 3 sin(n) 2cos(2n) n 3/2 a n =

sin(n) + 2 cos(2n) n 3/2 3 sin(n) 2cos(2n) n 3/2 a n = 60. Ratio ad root tests 60.1. Absolutely coverget series. Defiitio 13. (Absolute covergece) A series a is called absolutely coverget if the series of absolute values a is coverget. The absolute covergece

More information

Basic Sets. Functions. MTH299 - Examples. Example 1. Let S = {1, {2, 3}, 4}. Indicate whether each statement is true or false. (a) S = 4. (e) 2 S.

Basic Sets. Functions. MTH299 - Examples. Example 1. Let S = {1, {2, 3}, 4}. Indicate whether each statement is true or false. (a) S = 4. (e) 2 S. Basic Sets Example 1. Let S = {1, {2, 3}, 4}. Idicate whether each statemet is true or false. (a) S = 4 (b) {1} S (c) {2, 3} S (d) {1, 4} S (e) 2 S. (f) S = {1, 4, {2, 3}} (g) S Example 2. Compute the

More information

10-701/ Machine Learning Mid-term Exam Solution

10-701/ Machine Learning Mid-term Exam Solution 0-70/5-78 Machie Learig Mid-term Exam Solutio Your Name: Your Adrew ID: True or False (Give oe setece explaatio) (20%). (F) For a cotiuous radom variable x ad its probability distributio fuctio p(x), it

More information

THE SOLUTION OF NONLINEAR EQUATIONS f( x ) = 0.

THE SOLUTION OF NONLINEAR EQUATIONS f( x ) = 0. THE SOLUTION OF NONLINEAR EQUATIONS f( ) = 0. Noliear Equatio Solvers Bracketig. Graphical. Aalytical Ope Methods Bisectio False Positio (Regula-Falsi) Fied poit iteratio Newto Raphso Secat The root of

More information

Math 475, Problem Set #12: Answers

Math 475, Problem Set #12: Answers Math 475, Problem Set #12: Aswers A. Chapter 8, problem 12, parts (b) ad (d). (b) S # (, 2) = 2 2, sice, from amog the 2 ways of puttig elemets ito 2 distiguishable boxes, exactly 2 of them result i oe

More information

Seunghee Ye Ma 8: Week 5 Oct 28

Seunghee Ye Ma 8: Week 5 Oct 28 Week 5 Summary I Sectio, we go over the Mea Value Theorem ad its applicatios. I Sectio 2, we will recap what we have covered so far this term. Topics Page Mea Value Theorem. Applicatios of the Mea Value

More information

Math 299 Supplement: Real Analysis Nov 2013

Math 299 Supplement: Real Analysis Nov 2013 Math 299 Supplemet: Real Aalysis Nov 203 Algebra Axioms. I Real Aalysis, we work withi the axiomatic system of real umbers: the set R alog with the additio ad multiplicatio operatios +,, ad the iequality

More information

University of Colorado Denver Dept. Math. & Stat. Sciences Applied Analysis Preliminary Exam 13 January 2012, 10:00 am 2:00 pm. Good luck!

University of Colorado Denver Dept. Math. & Stat. Sciences Applied Analysis Preliminary Exam 13 January 2012, 10:00 am 2:00 pm. Good luck! Uiversity of Colorado Dever Dept. Math. & Stat. Scieces Applied Aalysis Prelimiary Exam 13 Jauary 01, 10:00 am :00 pm Name: The proctor will let you read the followig coditios before the exam begis, ad

More information

M A T H F A L L CORRECTION. Algebra I 1 4 / 1 0 / U N I V E R S I T Y O F T O R O N T O

M A T H F A L L CORRECTION. Algebra I 1 4 / 1 0 / U N I V E R S I T Y O F T O R O N T O M A T H 2 4 0 F A L L 2 0 1 4 HOMEWORK ASSIGNMENT #4 CORRECTION Algebra I 1 4 / 1 0 / 2 0 1 4 U N I V E R S I T Y O F T O R O N T O P r o f e s s o r : D r o r B a r - N a t a Correctio Homework Assigmet

More information

MATH 10550, EXAM 3 SOLUTIONS

MATH 10550, EXAM 3 SOLUTIONS MATH 155, EXAM 3 SOLUTIONS 1. I fidig a approximate solutio to the equatio x 3 +x 4 = usig Newto s method with iitial approximatio x 1 = 1, what is x? Solutio. Recall that x +1 = x f(x ) f (x ). Hece,

More information

Course 8 Properties of Regular Languages

Course 8 Properties of Regular Languages Course 8 Properties of Regular Laguages The structure ad the cotet of the lecture is based o http://www.eecs.wsu.edu/~aath/cpts37/lectures/idex.htm Topics ) How to prove whether a give laguage is ot regular?

More information

CS276A Practice Problem Set 1 Solutions

CS276A Practice Problem Set 1 Solutions CS76A Practice Problem Set Solutios Problem. (i) (ii) 8 (iii) 6 Compute the gamma-codes for the followig itegers: (i) (ii) 8 (iii) 6 Problem. For this problem, we will be dealig with a collectio of millio

More information

Regn. No. North Delhi : 33-35, Mall Road, G.T.B. Nagar (Opp. Metro Gate No. 3), Delhi-09, Ph: ,

Regn. No. North Delhi : 33-35, Mall Road, G.T.B. Nagar (Opp. Metro Gate No. 3), Delhi-09, Ph: , . Sectio-A cotais 30 Multiple Choice Questios (MCQ). Each questio has 4 choices (a), (b), (c) ad (d), for its aswer, out of which ONLY ONE is correct. From Q. to Q.0 carries Marks ad Q. to Q.30 carries

More information

MIDTERM 3 CALCULUS 2. Monday, December 3, :15 PM to 6:45 PM. Name PRACTICE EXAM SOLUTIONS

MIDTERM 3 CALCULUS 2. Monday, December 3, :15 PM to 6:45 PM. Name PRACTICE EXAM SOLUTIONS MIDTERM 3 CALCULUS MATH 300 FALL 08 Moday, December 3, 08 5:5 PM to 6:45 PM Name PRACTICE EXAM S Please aswer all of the questios, ad show your work. You must explai your aswers to get credit. You will

More information

1 Approximating Integrals using Taylor Polynomials

1 Approximating Integrals using Taylor Polynomials Seughee Ye Ma 8: Week 7 Nov Week 7 Summary This week, we will lear how we ca approximate itegrals usig Taylor series ad umerical methods. Topics Page Approximatig Itegrals usig Taylor Polyomials. Defiitios................................................

More information

Chapter 7 COMBINATIONS AND PERMUTATIONS. where we have the specific formula for the binomial coefficients:

Chapter 7 COMBINATIONS AND PERMUTATIONS. where we have the specific formula for the binomial coefficients: Chapter 7 COMBINATIONS AND PERMUTATIONS We have see i the previous chapter that (a + b) ca be writte as 0 a % a & b%þ% a & b %þ% b where we have the specific formula for the biomial coefficiets: '!!(&)!

More information

CHAPTER 1 SEQUENCES AND INFINITE SERIES

CHAPTER 1 SEQUENCES AND INFINITE SERIES CHAPTER SEQUENCES AND INFINITE SERIES SEQUENCES AND INFINITE SERIES (0 meetigs) Sequeces ad limit of a sequece Mootoic ad bouded sequece Ifiite series of costat terms Ifiite series of positive terms Alteratig

More information

A Note on the Symmetric Powers of the Standard Representation of S n

A Note on the Symmetric Powers of the Standard Representation of S n A Note o the Symmetric Powers of the Stadard Represetatio of S David Savitt 1 Departmet of Mathematics, Harvard Uiversity Cambridge, MA 0138, USA dsavitt@mathharvardedu Richard P Staley Departmet of Mathematics,

More information

4x 2. (n+1) x 3 n+1. = lim. 4x 2 n+1 n3 n. n 4x 2 = lim = 3

4x 2. (n+1) x 3 n+1. = lim. 4x 2 n+1 n3 n. n 4x 2 = lim = 3 Exam Problems (x. Give the series (, fid the values of x for which this power series coverges. Also =0 state clearly what the radius of covergece is. We start by settig up the Ratio Test: x ( x x ( x x

More information