CS115 INTRODUCTION TO COMPUTER SCIENCE 1. Additional Notes Module 5

Size: px
Start display at page:

Download "CS115 INTRODUCTION TO COMPUTER SCIENCE 1. Additional Notes Module 5"

Transcription

1 CS115 INTRODUCTION TO COMPUTER SCIENCE 1 Additional Notes Module 5

2 Example my-length (Slide 17) 2 (define (my-length alos) [(empty? alos) 0] [else (+ 1 (my-length (rest alos)))])) (my-length empty) alos is replaced with empty [(empty? empty) 0] [else (+ 1 (my-length (rest empty)))]) [true 0] [else (+ 1 (my-length (rest empty)))]) from Module 3, cond substitution rules: [true exp] ) exp 0

3 Example my-length (Slide 17) 3 (define (my-length alos) [(empty? alos) 0] [else (+ 1 (my-length (rest alos)))])) (my-length (cons 'a (cons 'b empty))) alos is replaced with (cons 'a (cons 'b empty))) [(empty? (cons 'a (cons 'b empty))) 0] [else (+ 1 (my-length (rest (cons 'a (cons 'b empty)))))]) [false 0] [else (+ 1 (my-length (rest (cons 'a (cons 'b empty)))))]) from Module 3, cond substitution rules: [false ][exp1 exp2] ) [exp1 exp2] )

4 Example my-length (Slide 17) 4 (define (my-length alos) [(empty? alos) 0] [else (+ 1 (my-length (rest alos)))])) (my-length (cons 'a (cons 'b empty))) [else (+ 1 (my-length (rest (cons 'a (cons 'b empty)))))]) from Module 3, cond substitution rules: [else exp]) exp (+ 1 (my-length (rest (cons 'a (cons 'b empty))))) (+ 1 (my-length (cons 'b empty))) we need to function my-length again

5 Example my-length (Slide 17) 5 (define (my-length alos) [(empty? alos) 0] [else (+ 1 (my-length (rest alos)))])) (my-length (cons 'a (cons 'b empty))) (+ 1 alos is replaced with (cons 'b empty)) [(empty? (cons 'b empty)) 0] [else (+ 1 (my-length (rest (cons 'b empty))))])) (+ 1 [false 0] [else (+ 1 (my-length (rest (cons 'b empty))))])) (+ 1 [else (+ 1 (my-length (rest (cons 'b empty))))]))

6 Example my-length (Slide 17) 6 (define (my-length alos) [(empty? alos) 0] [else (+ 1 (my-length (rest alos)))])) (my-length (cons 'a (cons 'b empty))) (+ 1 (+ 1 (my-length (rest (cons 'b empty))))) we need to function my-length again (+ 1 (+ 1 (my-length empty ))) (+ 1 (+ 1 [(empty? empty) 0] [else (+ 1 (my-length (rest empty)))])))

7 Example my-length (Slide 17) 7 (define (my-length alos) [(empty? alos) 0] [else (+ 1 (my-length (rest alos)))])) (my-length (cons 'a (cons 'b empty))) (+ 1 (+ 1 [true 0] [else (+ 1 (my-length (rest empty)))]))) (+ 1 (+ 1 0)) (+ 1 1) 2

8 Example: Factorial 8 f ( n) n f 1 ( n 1) if n else 0 base case f(2) = 2 x f(1) f(1) = 1 x f(0) f(0) = 1 Finally, to solve f(2), we now have: f(2) = 2 x 1 x 1 f(2) = 2

9 Example: Factorial 9 Visualizing Recursion using Factorial Example Example recursion trace: return 4 * 6 = 24 final answer recursivefactorial ( 4 ) return 3 * 2 = 6 recursivefactorial ( 3 ) return 2 * 1 = 2 recursivefactorial ( 2 ) return 1 * 1 = 1 recursivefactorial ( 1 ) return 1 recursivefactorial ( 0 ) Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, Data Structures and Algorithms in Java 6/e

10 Example: Factorial 10 In DrRacket: (define (f n) [(equal? n 0) 1] [else (* n (f (- n 1)))])) recursion base case f ( n) n f 1 ( n 1) if n else 0

11 Removing Elements from a List Slide (define (removal n alon) [(empty? alon) empty] [else example: remove first [(equal? (first alon) n) (removal n (rest alon))] [else (cons (first alon) (removal n (rest alon)))])])) is alon empty? Yes, return empty and exit removal function (base case) No, check the following cases: is n equal to first element in alon? Yes Call removal and pass the rest of alon No Create a new list with first element of alon and removal to check on the rest of the elements in alon

12 Example: Fibonacci 12 Fibonacci series: 0, 1, 1, 2, 3, 5, 8... Every number is the sum of the previous two f(0) = 0 f(1) = 1 base cases fib( n ) = fib( n - 1 ) + fib( n 2 ) Fibonacci in DrRacket (define (f n) [(or (equal? n 0) (equal? n 1)) n] [else (+ (f (- n 1)) (f (- n 2)))]))

CS 135 Winter 2018 Tutorial 7: Accumulative Recursion and Binary Trees. CS 135 Winter 2018 Tutorial 7: Accumulative Recursion and Binary Trees 1

CS 135 Winter 2018 Tutorial 7: Accumulative Recursion and Binary Trees. CS 135 Winter 2018 Tutorial 7: Accumulative Recursion and Binary Trees 1 CS 135 Winter 2018 Tutorial 7: Accumulative Recursion and Binary Trees CS 135 Winter 2018 Tutorial 7: Accumulative Recursion and Binary Trees 1 Goals of this tutorial You should be able to... understand

More information

Divide-and-Conquer. Divide-and conquer is a general algorithm design paradigm:

Divide-and-Conquer. Divide-and conquer is a general algorithm design paradigm: Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Merge Sort 7 2 9 4 2 4 7 9 7 2 2 7 9 4 4 9

More information

CS1 Recitation. Week 2

CS1 Recitation. Week 2 CS1 Recitation Week 2 Sum of Squares Write a function that takes an integer n n must be at least 0 Function returns the sum of the square of each value between 0 and n, inclusive Code: (define (square

More information

Recursion. The Recursion Pattern. Recursion 3/16/14

Recursion. The Recursion Pattern. Recursion 3/16/14 Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Recursion Recursion 1 The Recursion Pattern

More information

OVERVIEW. Recursion is an algorithmic technique where a function calls itself directly or indirectly. Why learn recursion?

OVERVIEW. Recursion is an algorithmic technique where a function calls itself directly or indirectly. Why learn recursion? CH. 5 RECURSION ACKNOWLEDGEMENT: THESE SLIDES ARE ADAPTED FROM SLIDES PROVIDED WITH DATA STRUCTURES AND ALGORITHMS IN JAVA, GOODRICH, TAMASSIA AND GOLDWASSER (WILEY 2016) OVERVIEW Recursion is an algorithmic

More information

Fall 2018 Discussion 8: October 24, 2018 Solutions. 1 Introduction. 2 Primitives

Fall 2018 Discussion 8: October 24, 2018 Solutions. 1 Introduction. 2 Primitives CS 6A Scheme Fall 208 Discussion 8: October 24, 208 Solutions Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write

More information

Deferred operations. Continuations Structure and Interpretation of Computer Programs. Tail recursion in action.

Deferred operations. Continuations Structure and Interpretation of Computer Programs. Tail recursion in action. Deferred operations Continuations 6.037 - Structure and Interpretation of Computer Programs Mike Phillips (define the-cons (cons 1 #f)) (set-cdr! the-cons the-cons) (define (run-in-circles l) (+

More information

RECURSION (CH 5) A pattern for solving algorithm design problems

RECURSION (CH 5) A pattern for solving algorithm design problems RECURSION (CH 5) A pattern for solving algorithm design problems Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H.,

More information

CS 314 Principles of Programming Languages. Lecture 16

CS 314 Principles of Programming Languages. Lecture 16 CS 314 Principles of Programming Languages Lecture 16 Zheng Zhang Department of Computer Science Rutgers University Friday 28 th October, 2016 Zheng Zhang 1 CS@Rutgers University Class Information Reminder:

More information

Functional Programming - 2. Higher Order Functions

Functional Programming - 2. Higher Order Functions Functional Programming - 2 Higher Order Functions Map on a list Apply Reductions: foldr, foldl Lexical scoping with let s Functional-11, CS5314, Sp16 BGRyder 1 Higher Order Functions Functions as 1st class

More information

CS 1101 Exam 3 A-Term 2013

CS 1101 Exam 3 A-Term 2013 NAME: CS 1101 Exam 3 A-Term 2013 Question 1: (55) Question 2: (20) Question 3: (25) TOTAL: (100) You have 50 minutes to complete this exam. You do not need to show templates, but you may receive partial

More information

Quick-Sort. Quick-sort is a randomized sorting algorithm based on the divide-and-conquer paradigm:

Quick-Sort. Quick-sort is a randomized sorting algorithm based on the divide-and-conquer paradigm: Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Quick-Sort 7 4 9 6 2 2 4 6 7 9 4 2 2 4 7 9

More information

Summer 2017 Discussion 10: July 25, Introduction. 2 Primitives and Define

Summer 2017 Discussion 10: July 25, Introduction. 2 Primitives and Define CS 6A Scheme Summer 207 Discussion 0: July 25, 207 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

More information

More About Recursive Data Types

More About Recursive Data Types More About Recursive Data Types CS 5010 Program Design Paradigms Bootcamp Lesson 5.5 Mitchell Wand, 2016-2017 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International

More information

Module 5: Lists. Readings: HtDP, Sections 9, 10.

Module 5: Lists. Readings: HtDP, Sections 9, 10. Module 5: Lists Readings: HtDP, Sections 9, 10. Lists are the main tool used in Racket to work with unbounded data. As with conditional expressions and structures, the data definition for lists leads naturally

More information

Homework 3: Recursion Due: 11:59 PM, Sep 25, 2018

Homework 3: Recursion Due: 11:59 PM, Sep 25, 2018 CS17 Integrated Introduction to Computer Science Klein Homework 3: Recursion Due: 11:59 PM, Sep 25, 2018 Contents 1 Factorial 3 2 Fibonacci 4 3 Odds Only 5 4 Increment All 6 5 Frequency 6 6 Sublist 7 6.1

More information

Interpreters and Tail Calls Fall 2017 Discussion 8: November 1, 2017 Solutions. 1 Calculator. calc> (+ 2 2) 4

Interpreters and Tail Calls Fall 2017 Discussion 8: November 1, 2017 Solutions. 1 Calculator. calc> (+ 2 2) 4 CS 61A Interpreters and Tail Calls Fall 2017 Discussion 8: November 1, 2017 Solutions 1 Calculator We are beginning to dive into the realm of interpreting computer programs that is, writing programs that

More information

Bucket-Sort and Radix-Sort

Bucket-Sort and Radix-Sort Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Bucket-Sort and Radix-Sort B 0 1 2 3 4 5 6

More information

CS 206 Introduction to Computer Science II

CS 206 Introduction to Computer Science II CS 206 Introduction to Computer Science II 03 / 05 / 2018 Instructor: Michael Eckmann Today s Topics Questions? Comments? binary search trees Finish delete method Discuss run times of various methods Michael

More information

Fall 2017 Discussion 7: October 25, 2017 Solutions. 1 Introduction. 2 Primitives

Fall 2017 Discussion 7: October 25, 2017 Solutions. 1 Introduction. 2 Primitives CS 6A Scheme Fall 207 Discussion 7: October 25, 207 Solutions Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write

More information

CS450 - Structure of Higher Level Languages

CS450 - Structure of Higher Level Languages Spring 2018 Streams February 24, 2018 Introduction Streams are abstract sequences. They are potentially infinite we will see that their most interesting and powerful uses come in handling infinite sequences.

More information

Recursion I and II. Discrete Structures (CS 173) Madhusudan Parthasarathy, University of Illinois 1

Recursion I and II. Discrete Structures (CS 173) Madhusudan Parthasarathy, University of Illinois 1 Recursion I and II Discrete Structures (CS 173) Madhusudan Parthasarathy, University of Illinois 1 Recursive definitions of functions 2 One simple form In other words, f(n) is defined in terms of f(n-1)

More information

Building a system for symbolic differentiation

Building a system for symbolic differentiation Computer Science 21b Structure and Interpretation of Computer Programs Building a system for symbolic differentiation Selectors, constructors and predicates: (constant? e) Is e a constant? (variable? e)

More information

Normal Order (Lazy) Evaluation SICP. Applicative Order vs. Normal (Lazy) Order. Applicative vs. Normal? How can we implement lazy evaluation?

Normal Order (Lazy) Evaluation SICP. Applicative Order vs. Normal (Lazy) Order. Applicative vs. Normal? How can we implement lazy evaluation? Normal Order (Lazy) Evaluation Alternative models for computation: Normal (Lazy) Order Evaluation Memoization Streams Applicative Order: evaluate all arguments, then apply operator Normal Order: pass unevaluated

More information

Bucket-Sort and Radix-Sort

Bucket-Sort and Radix-Sort Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Bucket-Sort and Radix-Sort 1, c 3, a 3, b

More information

RACKET BASICS, ORDER OF EVALUATION, RECURSION 1

RACKET BASICS, ORDER OF EVALUATION, RECURSION 1 RACKET BASICS, ORDER OF EVALUATION, RECURSION 1 COMPUTER SCIENCE 61AS 1. What is functional programming? Give an example of a function below: Functional Programming In functional programming, you do not

More information

CS 206 Introduction to Computer Science II

CS 206 Introduction to Computer Science II CS 206 Introduction to Computer Science II 03 / 09 / 2018 Instructor: Michael Eckmann Today s Topics Questions? Comments? More examples Change making algorithm Greedy algorithm Recursive implementation

More information

Chapter 1. Fundamentals of Higher Order Programming

Chapter 1. Fundamentals of Higher Order Programming Chapter 1 Fundamentals of Higher Order Programming 1 The Elements of Programming Any powerful language features: so does Scheme primitive data procedures combinations abstraction We will see that Scheme

More information

ITERATORS AND STREAMS 9

ITERATORS AND STREAMS 9 ITERATORS AND STREAMS 9 COMPUTER SCIENCE 61A November 12, 2015 1 Iterators An iterator is an object that tracks the position in a sequence of values. It can return an element at a time, and it is only

More information

An introduction to Scheme

An introduction to Scheme An introduction to Scheme Introduction A powerful programming language is more than just a means for instructing a computer to perform tasks. The language also serves as a framework within which we organize

More information

SCHEME 8. 1 Introduction. 2 Primitives COMPUTER SCIENCE 61A. March 23, 2017

SCHEME 8. 1 Introduction. 2 Primitives COMPUTER SCIENCE 61A. March 23, 2017 SCHEME 8 COMPUTER SCIENCE 61A March 2, 2017 1 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

More information

CS 314 Principles of Programming Languages

CS 314 Principles of Programming Languages CS 314 Principles of Programming Languages Lecture 16: Functional Programming Zheng (Eddy Zhang Rutgers University April 2, 2018 Review: Computation Paradigms Functional: Composition of operations on data.

More information

SCHEME 10 COMPUTER SCIENCE 61A. July 26, Warm Up: Conditional Expressions. 1. What does Scheme print? scm> (if (or #t (/ 1 0)) 1 (/ 1 0))

SCHEME 10 COMPUTER SCIENCE 61A. July 26, Warm Up: Conditional Expressions. 1. What does Scheme print? scm> (if (or #t (/ 1 0)) 1 (/ 1 0)) SCHEME 0 COMPUTER SCIENCE 6A July 26, 206 0. Warm Up: Conditional Expressions. What does Scheme print? scm> (if (or #t (/ 0 (/ 0 scm> (if (> 4 3 (+ 2 3 4 (+ 3 4 (* 3 2 scm> ((if (< 4 3 + - 4 00 scm> (if

More information

Comp 311: Sample Midterm Examination

Comp 311: Sample Midterm Examination Comp 311: Sample Midterm Examination October 29, 2007 Name: Id #: Instructions 1. The examination is closed book. If you forget the name for a Scheme operation, make up a name for it and write a brief

More information

Recursive Definitions

Recursive Definitions Recursion Objectives Explain the underlying concepts of recursion Examine recursive methods and unravel their processing steps Explain when recursion should and should not be used Demonstrate the use of

More information

SCHEME 7. 1 Introduction. 2 Primitives COMPUTER SCIENCE 61A. October 29, 2015

SCHEME 7. 1 Introduction. 2 Primitives COMPUTER SCIENCE 61A. October 29, 2015 SCHEME 7 COMPUTER SCIENCE 61A October 29, 2015 1 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

More information

INTERPRETERS 8. 1 Calculator COMPUTER SCIENCE 61A. November 3, 2016

INTERPRETERS 8. 1 Calculator COMPUTER SCIENCE 61A. November 3, 2016 INTERPRETERS 8 COMPUTER SCIENCE 61A November 3, 2016 1 Calculator We are beginning to dive into the realm of interpreting computer programs that is, writing programs that understand other programs. In

More information

Computer Science 21b (Spring Term, 2015) Structure and Interpretation of Computer Programs. Lexical addressing

Computer Science 21b (Spring Term, 2015) Structure and Interpretation of Computer Programs. Lexical addressing Computer Science 21b (Spring Term, 2015) Structure and Interpretation of Computer Programs Lexical addressing The difference between a interpreter and a compiler is really two points on a spectrum of possible

More information

PDF created with pdffactory Pro trial version Recursion

PDF created with pdffactory Pro trial version  Recursion Recursion Recursive procedures Recursion: A way of defining a concept where the text of the definition refers to the concept that is being defined. (Sounds like a buttery butter, but read on ) In programming:

More information

Streams, Delayed Evaluation and a Normal Order Interpreter. CS 550 Programming Languages Jeremy Johnson

Streams, Delayed Evaluation and a Normal Order Interpreter. CS 550 Programming Languages Jeremy Johnson Streams, Delayed Evaluation and a Normal Order Interpreter CS 550 Programming Languages Jeremy Johnson 1 Theme This lecture discusses the stream model of computation and an efficient method of implementation

More information

Working with recursion. From definition to template. Readings: HtDP, sections 11, 12, 13 (Intermezzo 2).

Working with recursion. From definition to template. Readings: HtDP, sections 11, 12, 13 (Intermezzo 2). Working with recursion Readings: HtDP, sections 11, 12, 13 (Intermezzo 2). We can extend the idea of a self-referential definition to defining the natural numbers, which leads to the use of recursion in

More information

Data Structures and Algorithms in Java

Data Structures and Algorithms in Java Data Structures and Algorithms in Java Sixth Edition Michael T. Goodrich Department of Computer Science University of California, Irvine Roberto Tamassia Department of Computer Science Brown University

More information

Working with recursion

Working with recursion Working with recursion Readings: HtDP, sections 11, 12, 13 (Intermezzo 2). We can extend the idea of a self-referential definition to defining the natural numbers, which leads to the use of recursion in

More information

Recursion & Iteration

Recursion & Iteration Recursion & Iteration York University Department of Computer Science and Engineering 1 Overview Recursion Examples Iteration Examples Iteration vs. Recursion Example [ref.: Chap 5,6 Wilensky] 2 Recursion

More information

Lecture 4. Hashing Methods

Lecture 4. Hashing Methods Lecture 4 Hashing Methods 1 Lecture Content 1. Basics 2. Collision Resolution Methods 2.1 Linear Probing Method 2.2 Quadratic Probing Method 2.3 Double Hashing Method 2.4 Coalesced Chaining Method 2.5

More information

Programming II (CS300)

Programming II (CS300) 1 Programming II (CS300) Chapter 9 (Part II) Recursion MOUNA KACEM Recursion: General Overview 2 Recursion in Algorithms Recursion is the use of recursive algorithms to solve a problem A recursive algorithm

More information

Halting Measures and Termination Arguments

Halting Measures and Termination Arguments Halting Measures and Termination Arguments CS 5010 Program Design Paradigms Bootcamp Lesson 8.2 Mitchell Wand, 2012-2015 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International

More information

A Brief Introduction to Scheme (II)

A Brief Introduction to Scheme (II) A Brief Introduction to Scheme (II) Philip W. L. Fong pwlfong@cs.uregina.ca Department of Computer Science University of Regina Regina, Saskatchewan, Canada Lists Scheme II p.1/29 Lists Aggregate data

More information

1.3. Conditional expressions To express case distinctions like

1.3. Conditional expressions To express case distinctions like Introduction Much of the theory developed in the underlying course Logic II can be implemented in a proof assistant. In the present setting this is interesting, since we can then machine extract from a

More information

Lab 7: OCaml 12:00 PM, Oct 22, 2017

Lab 7: OCaml 12:00 PM, Oct 22, 2017 CS17 Integrated Introduction to Computer Science Hughes Lab 7: OCaml 12:00 PM, Oct 22, 2017 Contents 1 Getting Started in OCaml 1 2 Pervasives Library 2 3 OCaml Basics 3 3.1 OCaml Types........................................

More information

CS 135 Winter 2018 Tutorial 8: Mutual recursion, nested lists, and local. CS 135 Winter 2018 Tutorial 8: Mutual recursion, nested lists, and local 1

CS 135 Winter 2018 Tutorial 8: Mutual recursion, nested lists, and local. CS 135 Winter 2018 Tutorial 8: Mutual recursion, nested lists, and local 1 CS 135 Winter 2018 Tutorial 8: Mutual recursion, nested lists, and local CS 135 Winter 2018 Tutorial 8: Mutual recursion, nested lists, and local 1 Goals of this tutorial You should be able to... write

More information

34. Recursion. Java. Summer 2008 Instructor: Dr. Masoud Yaghini

34. Recursion. Java. Summer 2008 Instructor: Dr. Masoud Yaghini 34. Recursion Java Summer 2008 Instructor: Dr. Masoud Yaghini Outline Introduction Example: Factorials Example: Fibonacci Numbers Recursion vs. Iteration References Introduction Introduction Recursion

More information

CMPSCI 250: Introduction to Computation. Lecture #14: Induction and Recursion (Still More Induction) David Mix Barrington 14 March 2013

CMPSCI 250: Introduction to Computation. Lecture #14: Induction and Recursion (Still More Induction) David Mix Barrington 14 March 2013 CMPSCI 250: Introduction to Computation Lecture #14: Induction and Recursion (Still More Induction) David Mix Barrington 14 March 2013 Induction and Recursion Three Rules for Recursive Algorithms Proving

More information

Module 3: New types of data

Module 3: New types of data Module 3: New types of data Readings: Sections 4 and 5 of HtDP. A Racket program applies functions to values to compute new values. These new values may in turn be supplied as arguments to other functions.

More information

Scheme in Scheme: The Metacircular Evaluator Eval and Apply

Scheme in Scheme: The Metacircular Evaluator Eval and Apply Scheme in Scheme: The Metacircular Evaluator Eval and Apply CS21b: Structure and Interpretation of Computer Programs Brandeis University Spring Term, 2015 The metacircular evaluator is A rendition of Scheme,

More information

Spring 2018 Discussion 7: March 21, Introduction. 2 Primitives

Spring 2018 Discussion 7: March 21, Introduction. 2 Primitives CS 61A Scheme Spring 2018 Discussion 7: March 21, 2018 1 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme

More information

Recursion CS GMU

Recursion CS GMU Recursion CS 112 @ GMU Recursion 2 Recursion recursion: something defined in terms of itself. function recursion: when a function calls itself. Sometimes this happens directly, sometimes indirectly. direct:

More information

CS 314 Principles of Programming Languages

CS 314 Principles of Programming Languages CS 314 Principles of Programming Languages Lecture 17: Functional Programming Zheng (Eddy Zhang Rutgers University April 4, 2018 Class Information Homework 6 will be posted later today. All test cases

More information

Programming II (CS300)

Programming II (CS300) 1 Programming II (CS300) Chapter 10 Recursion and Search MOUNA KACEM mouna@cs.wisc.edu Spring 2019 Recursion: General Overview 2 Recursion in Algorithms Recursion is the use of recursive algorithms to

More information

Local definitions and lexical scope

Local definitions and lexical scope Local definitions and lexical scope Readings: HtDP, Intermezzo 3 (Section 18). Language level: Intermediate Student CS 135 Winter 2018 09: Local definitions and lexical scope 1 Local definitions The functions

More information

Local definitions and lexical scope. Local definitions. Motivating local definitions. s(s a)(s b)(s c), where s = (a + b + c)/2.

Local definitions and lexical scope. Local definitions. Motivating local definitions. s(s a)(s b)(s c), where s = (a + b + c)/2. Local definitions and lexical scope Readings: HtDP, Intermezzo 3 (Section 18). Language level: Intermediate Student CS 135 Winter 2018 09: Local definitions and lexical scope 1 Local definitions The functions

More information

CS 5010 Program Design Paradigms Lesson 6.1

CS 5010 Program Design Paradigms Lesson 6.1 Lists vs. Structures CS 5010 Program Design Paradigms Lesson 6.1 Mitchell Wand, 2012-2016 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. 1 Module Introduction

More information

ALGORITHM DESIGN DYNAMIC PROGRAMMING. University of Waterloo

ALGORITHM DESIGN DYNAMIC PROGRAMMING. University of Waterloo ALGORITHM DESIGN DYNAMIC PROGRAMMING University of Waterloo LIST OF SLIDES 1-1 List of Slides 1 2 Dynamic Programming Approach 3 Fibonacci Sequence (cont.) 4 Fibonacci Sequence (cont.) 5 Bottom-Up vs.

More information

Delayed Expressions Fall 2017 Discussion 9: November 8, Iterables and Iterators. For Loops. Other Iterable Uses

Delayed Expressions Fall 2017 Discussion 9: November 8, Iterables and Iterators. For Loops. Other Iterable Uses CS 6A Delayed Expressions Fall 07 Discussion 9: November 8, 07 Iterables and Iterators An iterable is any container that can be processed sequentially. Examples include lists, tuples, strings, and dictionaries.

More information

Functional Programming Languages (FPL)

Functional Programming Languages (FPL) Functional Programming Languages (FPL) 1. Definitions... 2 2. Applications... 2 3. Examples... 3 4. FPL Characteristics:... 3 5. Lambda calculus (LC)... 4 6. Functions in FPLs... 7 7. Modern functional

More information

Lists. Readings: HtDP, sections 9 and 10. Avoid 10.3 (uses draw.ss). CS 135 Winter : Lists 1

Lists. Readings: HtDP, sections 9 and 10. Avoid 10.3 (uses draw.ss). CS 135 Winter : Lists 1 Lists Readings: HtDP, sections 9 and 10. Avoid 10.3 (uses draw.ss). CS 135 Winter 2018 05: Lists 1 Introducing lists Structures are useful for representing a fixed amount of data. But there are many circumstances

More information

Types of recursion. Structural vs. general recursion. Pure structural recursion. Readings: none. In this module: learn to use accumulative recursion

Types of recursion. Structural vs. general recursion. Pure structural recursion. Readings: none. In this module: learn to use accumulative recursion Types of recursion Readings: none. In this module: learn to use accumulative recursion learn to recognize generative recursion CS 135 Fall 2018 07: Types of recursion 1 Structural vs. general recursion

More information

Principles of Programming Languages Topic: Functional Programming Professor L. Thorne McCarty Spring 2003

Principles of Programming Languages Topic: Functional Programming Professor L. Thorne McCarty Spring 2003 Principles of Programming Languages Topic: Functional Programming Professor L. Thorne McCarty Spring 2003 CS 314, LS, LTM: Functional Programming 1 Scheme A program is an expression to be evaluated (in

More information

ormap, andmap, and filter

ormap, andmap, and filter ormap, andmap, and filter CS 5010 Program Design Paradigms Bootcamp Lesson 6.3 Mitchell Wand, 2012-2015 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

More information

Programming II (CS300)

Programming II (CS300) 1 Programming II (CS300) Chapter 10 Recursion and Search MOUNA KACEM Recursion: General Overview 2 Recursion in Algorithms Recursion is the use of recursive algorithms to solve a problem A recursive algorithm

More information

(Provisional) Lecture 08: List recursion and recursive diagrams 10:00 AM, Sep 22, 2017

(Provisional) Lecture 08: List recursion and recursive diagrams 10:00 AM, Sep 22, 2017 Integrated Introduction to Computer Science Hughes (Provisional) Lecture 08: List recursion and recursive diagrams 10:00 AM, Sep 22, 2017 Contents 1 Announcements 1 2 Evaluation Correction 1 3 Lists 2

More information

User-defined Functions. Conditional Expressions in Scheme

User-defined Functions. Conditional Expressions in Scheme User-defined Functions The list (lambda (args (body s to a function with (args as its argument list and (body as the function body. No quotes are needed for (args or (body. (lambda (x (+ x 1 s to the increment

More information

Types of recursion. Readings: none. In this module: a glimpse of non-structural recursion. CS 135 Winter : Types of recursion 1

Types of recursion. Readings: none. In this module: a glimpse of non-structural recursion. CS 135 Winter : Types of recursion 1 Types of recursion Readings: none. In this module: a glimpse of non-structural recursion CS 135 Winter 2018 07: Types of recursion 1 Structural vs. general recursion All of the recursion we have done to

More information

SCHEME The Scheme Interpreter. 2 Primitives COMPUTER SCIENCE 61A. October 29th, 2012

SCHEME The Scheme Interpreter. 2 Primitives COMPUTER SCIENCE 61A. October 29th, 2012 SCHEME COMPUTER SCIENCE 6A October 29th, 202 In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs, we will eventually

More information

Module 8: Local and functional abstraction

Module 8: Local and functional abstraction Module 8: Local and functional abstraction Readings: HtDP, Intermezzo 3 (Section 18); Sections 19-23. We will cover material on functional abstraction in a somewhat different order than the text. We will

More information

6.001 Notes: Section 4.1

6.001 Notes: Section 4.1 6.001 Notes: Section 4.1 Slide 4.1.1 In this lecture, we are going to take a careful look at the kinds of procedures we can build. We will first go back to look very carefully at the substitution model,

More information

COMP250: Stacks. Jérôme Waldispühl School of Computer Science McGill University. Based on slides from (Goodrich & Tamassia, 2004)

COMP250: Stacks. Jérôme Waldispühl School of Computer Science McGill University. Based on slides from (Goodrich & Tamassia, 2004) COMP250: Stacks Jérôme Waldispühl School of Computer Science McGill University Based on slides from (Goodrich & Tamassia, 2004) 2004 Goodrich, Tamassia The Stack ADT A Stack ADT is a list that allows only

More information

RECURSION. Problem Solving with Computers-II 6

RECURSION. Problem Solving with Computers-II 6 RECURSION Problem Solving with Computers-II 6 10 12 40 32 43 47 45 41 Let recursion draw you in. Many problems in Computer Science have a recursive structure Identify the recursive structure in these

More information

Data Structures And Algorithms

Data Structures And Algorithms Data Structures And Algorithms Recursion Eng. Anis Nazer First Semester 2016-2017 Recursion Recursion: to define something in terms of itself Example: factorial n!={ 1 n=0 n (n 1)! n>0 Recursion Example:

More information

q To develop recursive methods for recursive mathematical functions ( ).

q To develop recursive methods for recursive mathematical functions ( ). Chapter 8 Recursion CS: Java Programming Colorado State University Motivations Suppose you want to find all the files under a directory that contains a particular word. How do you solve this problem? There

More information

q To develop recursive methods for recursive mathematical functions ( ).

q To develop recursive methods for recursive mathematical functions ( ). /2/8 Chapter 8 Recursion CS: Java Programming Colorado State University Motivations Suppose you want to find all the files under a directory that contains a particular word. How do you solve this problem?

More information

Recursive definition: A definition that is defined in terms of itself. Recursive method: a method that calls itself (directly or indirectly).

Recursive definition: A definition that is defined in terms of itself. Recursive method: a method that calls itself (directly or indirectly). Recursion We teach recursion as the first topic, instead of new object-oriented ideas, so that those who are new to Java can have a chance to catch up on the object-oriented ideas from CS100. Recursive

More information

Local defini1ons. Func1on mul1ples- of

Local defini1ons. Func1on mul1ples- of Local defini1ons The func1ons and special forms we ve seen so far can be arbitrarily nested except define and check- expect. So far, defini.ons have to be made at the top level, outside any expression.

More information

YOUR NAME PLEASE: *** SOLUTIONS ***

YOUR NAME PLEASE: *** SOLUTIONS *** YOUR NAME PLEASE: *** SOLUTIONS *** Computer Science 201b SAMPLE Exam 1 SOLUTIONS February 15, 2015 Closed book and closed notes. No electronic devices. Show ALL work you want graded on the test itself.

More information

Scheme Tutorial. Introduction. The Structure of Scheme Programs. Syntax

Scheme Tutorial. Introduction. The Structure of Scheme Programs. Syntax Scheme Tutorial Introduction Scheme is an imperative language with a functional core. The functional core is based on the lambda calculus. In this chapter only the functional core and some simple I/O is

More information

CS 206 Introduction to Computer Science II

CS 206 Introduction to Computer Science II CS 206 Introduction to Computer Science II 03 / 25 / 2013 Instructor: Michael Eckmann Today s Topics Comments/Questions? More on Recursion Including Dynamic Programming technique Divide and Conquer techniques

More information

How to Design Programs

How to Design Programs How to Design Programs How to (in Racket): represent data variants trees and lists write functions that process the data See also http://www.htdp.org/ 1 Running Example: GUIs Pick a fruit: Apple Banana

More information

def F a c t o r i a l ( n ) : i f n == 1 : return 1 else : return n F a c t o r i a l ( n 1) def main ( ) : print ( F a c t o r i a l ( 4 ) )

def F a c t o r i a l ( n ) : i f n == 1 : return 1 else : return n F a c t o r i a l ( n 1) def main ( ) : print ( F a c t o r i a l ( 4 ) ) 116 4.5 Recursion One of the most powerful programming techniques involves a function calling itself; this is called recursion. It is not immediately obvious that this is useful; take that on faith for

More information

Functional Programming. Pure Functional Programming

Functional Programming. Pure Functional Programming Functional Programming Pure Functional Programming Computation is largely performed by applying functions to values. The value of an expression depends only on the values of its sub-expressions (if any).

More information

CS61A Notes Disc 11: Streams Streaming Along

CS61A Notes Disc 11: Streams Streaming Along CS61A Notes Disc 11: Streams Streaming Along syntax in lecture and in the book, so I will not dwell on that. Suffice it to say, streams is one of the most mysterious topics in CS61A, trust than whatever

More information

Streams and Evalutation Strategies

Streams and Evalutation Strategies Data and Program Structure Streams and Evalutation Strategies Lecture V Ahmed Rezine Linköpings Universitet TDDA69, VT 2014 Lecture 2: Class descriptions - message passing ( define ( make-account balance

More information

Lambda Calculus. CS 550 Programming Languages Jeremy Johnson

Lambda Calculus. CS 550 Programming Languages Jeremy Johnson Lambda Calculus CS 550 Programming Languages Jeremy Johnson 1 Lambda Calculus The semantics of a pure functional programming language can be mathematically described by a substitution process that mimics

More information

Two Approaches to Algorithms An Example (1) Iteration (2) Recursion

Two Approaches to Algorithms An Example (1) Iteration (2) Recursion 2. Recursion Algorithm Two Approaches to Algorithms (1) Iteration It exploits while-loop, for-loop, repeat-until etc. Classical, conventional, and general approach (2) Recursion Self-function call It exploits

More information

Graphs. Directed graphs. Readings: Section 28

Graphs. Directed graphs. Readings: Section 28 Graphs Readings: Section 28 CS 135 Winter 2018 12: Graphs 1 Directed graphs A directed graph consists of a collection of vertices (also called nodes) together with a collection of edges. An edge is an

More information

Module 8: Binary trees

Module 8: Binary trees Module 8: Binary trees Readings: HtDP, Section 14 We will cover the ideas in the text using different examples and different terminology. The readings are still important as an additional source of examples.

More information

Tail Recursion. ;; a recursive program for factorial (define fact (lambda (m) ;; m is non-negative (if (= m 0) 1 (* m (fact (- m 1))))))

Tail Recursion. ;; a recursive program for factorial (define fact (lambda (m) ;; m is non-negative (if (= m 0) 1 (* m (fact (- m 1)))))) Tail Recursion 1 Tail Recursion In place of loops, in a functional language one employs recursive definitions of functions. It is often easy to write such definitions, given a problem statement. Unfortunately,

More information

Module 10: General trees

Module 10: General trees Module 10: General trees Readings: HtDP, Sections 15 and 16 CS 115 Winter 2019 10: General trees 1 General trees Binary trees can be used for a large variety of application areas. One limitation is the

More information

Macros & Streams Spring 2018 Discussion 9: April 11, Macros

Macros & Streams Spring 2018 Discussion 9: April 11, Macros CS 61A Macros & Streams Spring 2018 Discussion 9: April 11, 2018 1 Macros So far, we ve mostly explored similarities between the Python and Scheme languages. For example, the Scheme list data structure

More information

Graphs. Readings: Section 28. CS 135 Fall : Graphs 1

Graphs. Readings: Section 28. CS 135 Fall : Graphs 1 Graphs Readings: Section 28 CS 135 Fall 2018 12: Graphs 1 Directed graphs A directed graph consists of a collection of vertices (also called nodes) together with a collection of edges. An edge is an ordered

More information

Problem Set CVO 103, Spring 2018

Problem Set CVO 103, Spring 2018 Problem Set CVO 103, Spring 2018 Hakjoo Oh Due: 06/12 (in class) Problem 1 The Fibonacci numbers can be defined as follows: 0 if n = 0 fib(n) = 1 if n = 1 fib(n 1) + fib(n 2) otherwise Write in OCaml the

More information