Inductive Definition to Recursive Function

Size: px
Start display at page:

Download "Inductive Definition to Recursive Function"

Transcription

1 PDS: CS Computer Sc & Egg: IIT Kharagpur 1 Iductive Defiitio to Recursive Fuctio

2 PDS: CS Computer Sc & Egg: IIT Kharagpur 2 Factorial Fuctio Cosider the followig recursive defiitio of the factorial fuctio.! = 1, if = 0, ( 1)!, if > 0. The fuctio is used to defie itself. The defiitio is a equatio with a computatioal couterpart.

3 PDS: CS Computer Sc & Egg: IIT Kharagpur 3 The Equatio The factorial fuctio satisfies the fuctioal equatio a. F() = 1, if = 0, F( 1), if > 0. a The factorial is the fixed-poit of this equatio

4 PDS: CS Computer Sc & Egg: IIT Kharagpur 4 Computatio of 4! 4! = 4 3! = 4 (3 2!) = 4 (3 (2 1!)) = 4 (3 (2 (1 0!))) = 4 (3 (2 (1 1))) = 4 (3 (2 1)) = 4 (3 2) = 4 6 = 24

5 PDS: CS Computer Sc & Egg: IIT Kharagpur 5 Note There is o value computatio i the first four steps. The fuctio is beig ufolded. The value computatio starts oly after the basis of the defiitio is reached. Last four steps computes the values.

6 PDS: CS Computer Sc & Egg: IIT Kharagpur 6 A fuctio i C Laguage may call itself A fuctio that calls itself directly or idirectly is called a recursive fuctio. Ufoldig ad delayed computatio ca be simulated by such a fuctio.

7 PDS: CS Computer Sc & Egg: IIT Kharagpur 7 Recursive Call If a fuctio calls itself, the obvious questio is about the termiatio of the process. A() call A A() call A A() call A A()

8 PDS: CS Computer Sc & Egg: IIT Kharagpur 8 Recursive Call Naturally the call caot be ucoditioal. The basis of a iductive (recursive) defiitio provides the coditio for termiatio. The fuctio calls itself to reach the termiatio coditio, the basis.

9 PDS: CS Computer Sc & Egg: IIT Kharagpur 9 Useless for Computatio The factorial fuctio also satisfies the followig equatio, F() = 1, if = 0, F(+1) +1, if > 0. but caot be used for computatio as a call to itself does ot reach the basis.

10 PDS: CS Computer Sc & Egg: IIT Kharagpur 10 Recursive factorial Fuctio it factorial(it ) { if ( == 0) retur 1 ; else retur *factorial( - 1) ; } // factorialfr1.c The fuctio takes a actual parameter p (a o-egative iteger) ad returs the value of p!.

11 PDS: CS Computer Sc & Egg: IIT Kharagpur 11 Differet Calls ad Icaratios of Actual Parameter is 4

12 PDS: CS Computer Sc & Egg: IIT Kharagpur d Call : 3 1 3rd Call : th Call :!= 0 * th Call :!= 0 * 0 Retur 1 First Call : 4 1 3!= 0 * Retur 2 4!= 0 Retur * 6 Retur 24 1 Retur 1

13 PDS: CS Computer Sc & Egg: IIT Kharagpur 13 Same Code Differet Data Same code is used for every recursive call. Data (local) is differet i every recursive call.

14 PDS: CS Computer Sc & Egg: IIT Kharagpur 14 Code for factorial computatio mai() stack frame factorial() factorial() factorial() factorial() factorial() stack frams High address p retur address retur address retur address retur address retur address Text/Code Regio of Memory Low address Stack Regio of Memory

15 PDS: CS Computer Sc & Egg: IIT Kharagpur 15 Note The first phase does ot compute the value but ufolds the recursio upto the base case. The value computatio starts from the base case.

16 PDS: CS Computer Sc & Egg: IIT Kharagpur 16 Note For every call there is ew icaratio of all the formal parameters ad the local variables (that are ot static). The variable ames get bid to differet memory locatios. Variables of oe ivocatio are ot visible from aother ivocatio.

17 PDS: CS Computer Sc & Egg: IIT Kharagpur 17 Note Oce a retur statemet is executed, all the variables of the correspodig ivocatio die. The last icaratio of a variable ame dies first - last i first out (LIFO). The last call is retured first.

18 PDS: CS Computer Sc & Egg: IIT Kharagpur 18 Stack Frame or Activatio Record mai() stack frame factorial(4) stack frame 4 x retur address mai() stack frame factorial(4) factorial(3) 4 3 x retur address retur address Stack Regio Stack Regio

19 PDS: CS Computer Sc & Egg: IIT Kharagpur 19 mai() stack frame factorial(4) factorial(3) factorial(2) factorial(1) factorial(0) retur addr 4 x retur address retur address retur address retur address Retur 1 mai() stack frame factorial(4) factorial(3) factorial(2) factorial(1) 4 x retur address retur address retur address retur address Stack Regio Stack Regio

20 PDS: CS Computer Sc & Egg: IIT Kharagpur 20 mai() stack frame factorial(4) factorial(3) factorial(2) factorial(1) retur addr 4 x retur address retur address retur address mai() stack frame factorial(4) factorial(3) factorial(2) Retur 1 4 x 3 2 retur address retur address retur address Stack Regio Stack Regio

21 PDS: CS Computer Sc & Egg: IIT Kharagpur 21 mai() stack frame x mai() stack frame x factorial(4) retur addr 4 Retur 24 Stack Regio Stack Regio

22 PDS: CS Computer Sc & Egg: IIT Kharagpur 22 Note The recursive factorial fuctio uses more memory tha its o-recursive couter part. The o-recursive fuctio uses fixed amout of memory for a it data, whereas the memory usage by the recursive fuctio is proportioal to the value of data. Moreover a fuctio call ad retur takes some amout of extra time.

23 PDS: CS Computer Sc & Egg: IIT Kharagpur 23 Recursive Fuctio with Iterative Dyamics We have see that the value computatio i our factorial fuctio starts after ufoldig the recursio. But this dyamics of computatio i a recursive fuctio ca be made differet. The fuctio may start the computatio from the begiig.

24 PDS: CS Computer Sc & Egg: IIT Kharagpur 24 it factiter(it, it acc) { if ( == 0) retur acc ; else retur factiter(-1, *acc) ; } // factorialfr2.c This fuctio is called as factiter(, 1) to calculate the value of!. The secod parameter is the value of the basis.

25 PDS: CS Computer Sc & Egg: IIT Kharagpur 25 Computatio of factiter(4,1)

26 PDS: CS Computer Sc & Egg: IIT Kharagpur 26 4th Call : 5th Call : First Call : 4 1 4*1 2d Call : acc 3 4!= *4 3rd Call : acc 2 1 2* acc!= != 0 Retur *24 acc 0 24 Retur acc 4 1 Retur 24!= 0 Retur 24 Retur 24

27 PDS: CS Computer Sc & Egg: IIT Kharagpur 27 mai() stack frame factiter() stack frams factiter() factiter() factiter() High address p acc retur address acc retur address 12 2 acc retur address 1 24 acc retur address Low address Stack Regio

28 PDS: CS Computer Sc & Egg: IIT Kharagpur 28 Note The computatio of! i this recursive fuctio is very similar to the computatio i a for-loop. it factiter(it ) { it acc = 1, i ; } for(i = ; i > 0 ; --i) acc *= i ; retur acc ;

29 PDS: CS Computer Sc & Egg: IIT Kharagpur 29 Iductive Defiitio: gcd(m, ) gcd(m,) = if m = 0, gcd( mod m,m) if m > 0.

30 PDS: CS Computer Sc & Egg: IIT Kharagpur 30 Recursive Fuctio gcd(m,) it gcd(it s, it l){ if(s == 0) retur l; retur gcd(l%s, s); } // gcdfr.c

31 PDS: CS Computer Sc & Egg: IIT Kharagpur 31 Differet Calls to gcd() gcd(0,0) retur 0 gcd(0,5) retur 5 gcd(5,0) gcd(0,5) retur 5 gcd(18, 12) gcd(12, 18) gcd(6,12) gcd(0,6) retur 6

Inductive Definition to Recursive Function

Inductive Definition to Recursive Function C Programming 1 Inductive Definition to Recursive Function C Programming 2 Factorial Function Consider the following recursive definition of the factorial function. 1, if n = 0, n! = n (n 1)!, if n > 0.

More information

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1 COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,

More information

Recursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle:

Recursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle: Recursio Recursio Jordi Cortadella Departmet of Computer Sciece Priciple: Reduce a complex problem ito a simpler istace of the same problem Recursio Itroductio to Programmig Dept. CS, UPC 2 Mathematical

More information

Last class. n Scheme. n Equality testing. n eq? vs. equal? n Higher-order functions. n map, foldr, foldl. n Tail recursion

Last class. n Scheme. n Equality testing. n eq? vs. equal? n Higher-order functions. n map, foldr, foldl. n Tail recursion Aoucemets HW6 due today HW7 is out A team assigmet Submitty page will be up toight Fuctioal correctess: 75%, Commets : 25% Last class Equality testig eq? vs. equal? Higher-order fuctios map, foldr, foldl

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

Programming & Data Structure

Programming & Data Structure Functions Programming & Data Structure CS 11002 Partha Bhowmick http://cse.iitkgp.ac.in/ pb CSE Department IIT Kharagpur Spring 2012-2013 Functions Callee : Caller = Bowler : Captain Functions int f(){...

More information

Homework 1 Solutions MA 522 Fall 2017

Homework 1 Solutions MA 522 Fall 2017 Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

Recursion. Computer Science S-111 Harvard University David G. Sullivan, Ph.D. Review: Method Frames

Recursion. Computer Science S-111 Harvard University David G. Sullivan, Ph.D. Review: Method Frames Uit 4, Part 3 Recursio Computer Sciece S-111 Harvard Uiversity David G. Sulliva, Ph.D. Review: Method Frames Whe you make a method call, the Java rutime sets aside a block of memory kow as the frame of

More information

Basic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000.

Basic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000. 5-23 The course that gives CM its Zip Memory Maagemet II: Dyamic Storage Allocatio Mar 6, 2000 Topics Segregated lists Buddy system Garbage collectio Mark ad Sweep Copyig eferece coutig Basic allocator

More information

5.3 Recursive definitions and structural induction

5.3 Recursive definitions and structural induction /8/05 5.3 Recursive defiitios ad structural iductio CSE03 Discrete Computatioal Structures Lecture 6 A recursively defied picture Recursive defiitios e sequece of powers of is give by a = for =0,,, Ca

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria.

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria. Computer Sciece Foudatio Exam August, 005 Computer Sciece Sectio A No Calculators! Name: SSN: KEY Solutios ad Gradig Criteria Score: 50 I this sectio of the exam, there are four (4) problems. You must

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

Algorithm. Counting Sort Analysis of Algorithms

Algorithm. Counting Sort Analysis of Algorithms Algorithm Coutig Sort Aalysis of Algorithms Assumptios: records Coutig sort Each record cotais keys ad data All keys are i the rage of 1 to k Space The usorted list is stored i A, the sorted list will

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute

More information

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen COP4020 Programmig Laguages Fuctioal Programmig Prof. Robert va Egele Overview What is fuctioal programmig? Historical origis of fuctioal programmig Fuctioal programmig today Cocepts of fuctioal programmig

More information

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis

More information

Lecture 9: Exam I Review

Lecture 9: Exam I Review CS 111 (Law): Program Desig I Lecture 9: Exam I Review Robert H. Sloa & Richard Warer Uiversity of Illiois, Chicago September 22, 2016 This Class Discuss midterm topics Go over practice examples Aswer

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

Matrix representation of a solution of a combinatorial problem of the group theory

Matrix representation of a solution of a combinatorial problem of the group theory Matrix represetatio of a solutio of a combiatorial problem of the group theory Krasimir Yordzhev, Lilyaa Totia Faculty of Mathematics ad Natural Scieces South-West Uiversity 66 Iva Mihailov Str, 2700 Blagoevgrad,

More information

CS 111: Program Design I Lecture # 7: First Loop, Web Crawler, Functions

CS 111: Program Design I Lecture # 7: First Loop, Web Crawler, Functions CS 111: Program Desig I Lecture # 7: First Loop, Web Crawler, Fuctios Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago September 18, 2018 What will this prit? x = 5 if x == 3: prit("hi!")

More information

CS211 Fall 2003 Prelim 2 Solutions and Grading Guide

CS211 Fall 2003 Prelim 2 Solutions and Grading Guide CS11 Fall 003 Prelim Solutios ad Gradig Guide Problem 1: (a) obj = obj1; ILLEGAL because type of referece must always be a supertype of type of object (b) obj3 = obj1; ILLEGAL because type of referece

More information

Arithmetic Sequences

Arithmetic Sequences . Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered

More information

Lecture 5: Recursion. Recursion Overview. Recursion is a powerful technique for specifying funclons, sets, and programs

Lecture 5: Recursion. Recursion Overview. Recursion is a powerful technique for specifying funclons, sets, and programs CS/ENGRD 20 Object- Orieted Programmig ad Data Structures Sprig 202 Doug James Visual Recursio Lecture : Recursio http://seredip.brymawr.edu/exchage/files/authors/faculty/39/literarykids/ifiite_mirror.jpg!

More information

High-Order Language APPLICATION LEVEL HIGH-ORDER LANGUAGE LEVEL ASSEMBLY LEVEL OPERATING SYSTEM LEVEL INSTRUCTION SET ARCHITECTURE LEVEL

High-Order Language APPLICATION LEVEL HIGH-ORDER LANGUAGE LEVEL ASSEMBLY LEVEL OPERATING SYSTEM LEVEL INSTRUCTION SET ARCHITECTURE LEVEL Chapter 2 C High-Order Laguage APPLICATION LEVEL HIGH-ORDER LANGUAGE LEVEL 6 ASSEMBLY LEVEL OPERATING SYSTEM LEVEL INSTRUCTION SET ARCHITECTURE LEVEL MICROCODE LEVEL LOGIC GATE LEVEL Figure 2. 7 6 5 4

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

top() Applications of Stacks

top() Applications of Stacks CS22 Algorithms ad Data Structures MW :00 am - 2: pm, MSEC 0 Istructor: Xiao Qi Lecture 6: Stacks ad Queues Aoucemets Quiz results Homework 2 is available Due o September 29 th, 2004 www.cs.mt.edu~xqicoursescs22

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

6.854J / J Advanced Algorithms Fall 2008

6.854J / J Advanced Algorithms Fall 2008 MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

CS 11 C track: lecture 1

CS 11 C track: lecture 1 CS 11 C track: lecture 1 Prelimiaries Need a CMS cluster accout http://acctreq.cms.caltech.edu/cgi-bi/request.cgi Need to kow UNIX IMSS tutorial liked from track home page Track home page: http://courses.cms.caltech.edu/courses/cs11/material

More information

Solutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer

Solutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer Departmet of Computer ciece Columbia Uiversity olutios to Fial COM W45 Programmig Laguages ad Traslators Moday, May 4, 2009 4:0-5:25pm, 309 Havemeyer Closed book, o aids. Do questios 5. Each questio is

More information

Weston Anniversary Fund

Weston Anniversary Fund Westo Olie Applicatio Guide 2018 1 This guide is desiged to help charities applyig to the Westo to use our olie applicatio form. The Westo is ope to applicatios from 5th Jauary 2018 ad closes o 30th Jue

More information

Project 2.5 Improved Euler Implementation

Project 2.5 Improved Euler Implementation Project 2.5 Improved Euler Implemetatio Figure 2.5.10 i the text lists TI-85 ad BASIC programs implemetig the improved Euler method to approximate the solutio of the iitial value problem dy dx = x+ y,

More information

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1 CS200: Hash Tables Prichard Ch. 13.2 CS200 - Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted array-based Usorted array-based Balaced Search Trees O(log ) O() O() O() O(1) O()

More information

ifs considered Harmful

ifs considered Harmful ifs cosidered Harmful Or; how to elimiate 90% of your bugs ad 99% of your techical debt i oe easy step. Jules May JulesMay.co.uk Codebase survey Bugs: all ~2.5 millio lies 5 years developmet 6-25 developers

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The

More information

Computational Geometry

Computational Geometry Computatioal Geometry Chapter 4 Liear programmig Duality Smallest eclosig disk O the Ageda Liear Programmig Slides courtesy of Craig Gotsma 4. 4. Liear Programmig - Example Defie: (amout amout cosumed

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

Elementary Educational Computer

Elementary Educational Computer Chapter 5 Elemetary Educatioal Computer. Geeral structure of the Elemetary Educatioal Computer (EEC) The EEC coforms to the 5 uits structure defied by vo Neuma's model (.) All uits are preseted i a simplified

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The

More information

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence?

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence? 6. Recursive Procedures I Sectio 6.1, you used fuctio otatio to write a explicit formula to determie the value of ay term i a Sometimes it is easier to calculate oe term i a sequece usig the previous terms.

More information

Reliable Transmission. Spring 2018 CS 438 Staff - University of Illinois 1

Reliable Transmission. Spring 2018 CS 438 Staff - University of Illinois 1 Reliable Trasmissio Sprig 2018 CS 438 Staff - Uiversity of Illiois 1 Reliable Trasmissio Hello! My computer s ame is Alice. Alice Bob Hello! Alice. Sprig 2018 CS 438 Staff - Uiversity of Illiois 2 Reliable

More information

Overview Recursion and Induction

Overview Recursion and Induction Oveiew Recursio ad Iductio Recursio a strategy for writig rograms that comute i a divide-ad-coquer fashio solve a large rolem y reakig it u ito smaller rolems of same kid Iductio a mathematical strategy

More information

Overview Recursion and Induction

Overview Recursion and Induction Overview Recursio ad Iductio Recursio a strategy for writig rograms that comute i a divide-ad-coquer fashio solve a large rolem y reakig it u ito smaller rolems of same kid Iductio a mathematical strategy

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

Guide to Applying Online

Guide to Applying Online Guide to Applyig Olie Itroductio Respodig to requests for additioal iformatio Reportig: submittig your moitorig or ed of grat Pledges: submittig your Itroductio This guide is to help charities submit their

More information

Examples and Applications of Binary Search

Examples and Applications of Binary Search Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We

More information

Threads and Concurrency in Java: Part 1

Threads and Concurrency in Java: Part 1 Cocurrecy Threads ad Cocurrecy i Java: Part 1 What every computer egieer eeds to kow about cocurrecy: Cocurrecy is to utraied programmers as matches are to small childre. It is all too easy to get bured.

More information

Threads and Concurrency in Java: Part 1

Threads and Concurrency in Java: Part 1 Threads ad Cocurrecy i Java: Part 1 1 Cocurrecy What every computer egieer eeds to kow about cocurrecy: Cocurrecy is to utraied programmers as matches are to small childre. It is all too easy to get bured.

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

Alpha Individual Solutions MAΘ National Convention 2013

Alpha Individual Solutions MAΘ National Convention 2013 Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5

More information

CIS 121. Introduction to Trees

CIS 121. Introduction to Trees CIS 121 Itroductio to Trees 1 Tree ADT Tree defiitio q A tree is a set of odes which may be empty q If ot empty, the there is a distiguished ode r, called root ad zero or more o-empty subtrees T 1, T 2,

More information

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig

More information

A graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n))

A graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n)) ca see that time required to search/sort grows with size of We How do space/time eeds of program grow with iput size? iput. time: cout umber of operatios as fuctio of iput Executio size operatio Assigmet:

More information

n Haskell n Syntax n Lazy evaluation n Static typing and type inference n Algebraic data types n Pattern matching n Type classes

n Haskell n Syntax n Lazy evaluation n Static typing and type inference n Algebraic data types n Pattern matching n Type classes Aoucemets Quiz 7 HW 9 is due o Friday Raibow grades HW 1-6 plus 8. Please, read our commets o 8! Exam 1-2 Quiz 1-6 Ay questios/cocers, let us kow ASAP Last Class Haskell Sytax Lazy evaluatio Static typig

More information

FFT: An example of complex combinational circuits

FFT: An example of complex combinational circuits Lecture from 6.s195 taught i Fall 2013 Costructive Computer Architecture FFT: A example of complex combiatioal circuits Arvid Computer Sciece & Artificial Itelligece Lab. Massachusetts Istitute of Techology

More information

Some New Results on Prime Graphs

Some New Results on Prime Graphs Ope Joural of Discrete Mathematics, 202, 2, 99-04 http://dxdoiorg/0426/ojdm202209 Published Olie July 202 (http://wwwscirporg/joural/ojdm) Some New Results o Prime Graphs Samir Vaidya, Udaya M Prajapati

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 4 Procedural Abstractio ad Fuctios That Retur a Value Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 4.1 Top-Dow Desig 4.2 Predefied Fuctios 4.3 Programmer-Defied Fuctios 4.4

More information

It just came to me that I 8.2 GRAPHS AND CONVERGENCE

It just came to me that I 8.2 GRAPHS AND CONVERGENCE 44 Chapter 8 Discrete Mathematics: Fuctios o the Set of Natural Numbers (a) Take several odd, positive itegers for a ad write out eough terms of the 3N sequece to reach a repeatig loop (b) Show that ot

More information

Counting Regions in the Plane and More 1

Counting Regions in the Plane and More 1 Coutig Regios i the Plae ad More 1 by Zvezdelia Stakova Berkeley Math Circle Itermediate I Group September 016 1. Overarchig Problem Problem 1 Regios i a Circle. The vertices of a polygos are arraged o

More information

Lecture 28: Data Link Layer

Lecture 28: Data Link Layer Automatic Repeat Request (ARQ) 2. Go ack N ARQ Although the Stop ad Wait ARQ is very simple, you ca easily show that it has very the low efficiecy. The low efficiecy comes from the fact that the trasmittig

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

ENGR Spring Exam 1

ENGR Spring Exam 1 ENGR 300 Sprig 03 Exam INSTRUCTIONS: Duratio: 60 miutes Keep your eyes o your ow work! Keep your work covered at all times!. Each studet is resposible for followig directios. Read carefully.. MATLAB ad

More information

DATA STRUCTURES. amortized analysis binomial heaps Fibonacci heaps union-find. Data structures. Appetizer. Appetizer

DATA STRUCTURES. amortized analysis binomial heaps Fibonacci heaps union-find. Data structures. Appetizer. Appetizer Data structures DATA STRUCTURES Static problems. Give a iput, produce a output. Ex. Sortig, FFT, edit distace, shortest paths, MST, max-flow,... amortized aalysis biomial heaps Fiboacci heaps uio-fid Dyamic

More information

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein 068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig

More information

COP4020 Programming Languages. Subroutines and Parameter Passing Prof. Robert van Engelen

COP4020 Programming Languages. Subroutines and Parameter Passing Prof. Robert van Engelen COP4020 Programmig Laguages Subrouties ad Parameter Passig Prof. Robert va Egele Overview Parameter passig modes Subroutie closures as parameters Special-purpose parameters Fuctio returs COP4020 Fall 2016

More information

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad

More information

Recursion. A problem solving technique where an algorithm is defined in terms of itself. A recursive method is a method that calls itself

Recursion. A problem solving technique where an algorithm is defined in terms of itself. A recursive method is a method that calls itself Recursio 1 A probem sovig techique where a agorithm is defied i terms of itsef A recursive method is a method that cas itsef A recursive agorithm breaks dow the iput or the search space ad appies the same

More information

Major CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70

Major CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70 NOTE:. Attempt all seve questios. Major CSL 02 2. Write your ame ad etry o o every sheet of the aswer script. Time 2 Hrs Max Marks 70 Q No Q Q 2 Q 3 Q 4 Q 5 Q 6 Q 7 Total MM 6 2 4 0 8 4 6 70 Q. Write a

More information

A Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions

A Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions Proceedigs of the 10th WSEAS Iteratioal Coferece o APPLIED MATHEMATICS, Dallas, Texas, USA, November 1-3, 2006 316 A Geeralized Set Theoretic Approach for Time ad Space Complexity Aalysis of Algorithms

More information

WORKED EXAMPLE 7.1. Producing a Mass Mailing. We want to automate the process of producing mass mailings. A typical letter might look as follows:

WORKED EXAMPLE 7.1. Producing a Mass Mailing. We want to automate the process of producing mass mailings. A typical letter might look as follows: Worked Example 7.1 Producig a Mass Mailig 1 WORKED EXAMPLE 7.1 Producig a Mass Mailig We wat to automate the process of producig mass mailigs. A typical letter might look as follows: To: Ms. Sally Smith

More information

Math Section 2.2 Polynomial Functions

Math Section 2.2 Polynomial Functions Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably

More information

Abstract Data Types (ADTs) Stacks. The Stack ADT ( 4.2) Stack Interface in Java

Abstract Data Types (ADTs) Stacks. The Stack ADT ( 4.2) Stack Interface in Java Abstract Data Types (ADTs) tacks A abstract data type (ADT) is a abstractio of a data structure A ADT specifies: Data stored Operatios o the data Error coditios associated with operatios Example: ADT modelig

More information

Graceful Labelings of Pendant Graphs

Graceful Labelings of Pendant Graphs Rose-Hulma Udergraduate Mathematics Joural Volume Issue Article 0 Graceful Labeligs of Pedat Graphs Alessadra Graf Norther Arizoa Uiversity, ag@au.edu Follow this ad additioal works at: http://scholar.rose-hulma.edu/rhumj

More information

Some cycle and path related strongly -graphs

Some cycle and path related strongly -graphs Some cycle ad path related strogly -graphs I. I. Jadav, G. V. Ghodasara Research Scholar, R. K. Uiversity, Rajkot, Idia. H. & H. B. Kotak Istitute of Sciece,Rajkot, Idia. jadaviram@gmail.com gaurag ejoy@yahoo.co.i

More information

OCR Statistics 1. Working with data. Section 3: Measures of spread

OCR Statistics 1. Working with data. Section 3: Measures of spread Notes ad Eamples OCR Statistics 1 Workig with data Sectio 3: Measures of spread Just as there are several differet measures of cetral tedec (averages), there are a variet of statistical measures of spread.

More information

Mathematical Stat I: solutions of homework 1

Mathematical Stat I: solutions of homework 1 Mathematical Stat I: solutios of homework Name: Studet Id N:. Suppose we tur over cards simultaeously from two well shuffled decks of ordiary playig cards. We say we obtai a exact match o a particular

More information

COMP Parallel Computing. PRAM (1): The PRAM model and complexity measures

COMP Parallel Computing. PRAM (1): The PRAM model and complexity measures COMP 633 - Parallel Computig Lecture 2 August 24, 2017 : The PRAM model ad complexity measures 1 First class summary This course is about parallel computig to achieve high-er performace o idividual problems

More information

Module 8-7: Pascal s Triangle and the Binomial Theorem

Module 8-7: Pascal s Triangle and the Binomial Theorem Module 8-7: Pascal s Triagle ad the Biomial Theorem Gregory V. Bard April 5, 017 A Note about Notatio Just to recall, all of the followig mea the same thig: ( 7 7C 4 C4 7 7C4 5 4 ad they are (all proouced

More information

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,

More information

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors

More information

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup   Note: Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values

More information

27 Refraction, Dispersion, Internal Reflection

27 Refraction, Dispersion, Internal Reflection Chapter 7 Refractio, Dispersio, Iteral Reflectio 7 Refractio, Dispersio, Iteral Reflectio Whe we talked about thi film iterferece, we said that whe light ecouters a smooth iterface betwee two trasparet

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19 CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.

More information

Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where

Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where ON MAXIMUM CHORDAL SUBGRAPH * Paul Erdos Mathematical Istitute of the Hugaria Academy of Scieces ad Reu Laskar Clemso Uiversity 1. Let G() deote a udirected graph, with vertices ad V(G) deote the vertex

More information

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions:

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions: CS 604 Data Structures Midterm Sprig, 00 VIRG INIA POLYTECHNIC INSTITUTE AND STATE U T PROSI M UNI VERSI TY Istructios: Prit your ame i the space provided below. This examiatio is closed book ad closed

More information

Civil Engineering Computation

Civil Engineering Computation Civil Egieerig Computatio Fidig Roots of No-Liear Equatios March 14, 1945 World War II The R.A.F. first operatioal use of the Grad Slam bomb, Bielefeld, Germay. Cotets 2 Root basics Excel solver Newto-Raphso

More information