ALGORITHMS ALGORITHMS ALGORITHMS ALGORITHMS ALGORITHMS ALGORITHMS. Reference: Discrete Math by Rosen

Size: px
Start display at page:

Download "ALGORITHMS ALGORITHMS ALGORITHMS ALGORITHMS ALGORITHMS ALGORITHMS. Reference: Discrete Math by Rosen"

Transcription

1 Reference: Discrete Math by Rosen Example: Finding the Maximum Element in a Finite Sequence. procedure max(a 1, a 2,..., a n : integers) max : = a 1 f i : = 2 to n if max < a i then max := a i return max {max is the largest element} 1 2 Introduction There are many general classes of problems that arise in discrete mathematics. F instance: given a sequence of integers, find the largest one; given a set, list all its subsets; given a set of integers, put them in increasing der; given a netwk, find the shtest path between two vertices. 3 When presented with such a problem, the first thing to do is to construct a model that translates the problem into a mathematical context. Setting up the appropriate mathematical model is only part of the solution. To complete the solution, a method is needed that will solve the general problem using the model. Ideally, what is required is a procedure that follows a sequence of steps that leads to the desired answer. 4 An algithm is a finite sequence of precise instructions f perfming a computation f solving a problem. 5 Describe an algithm f finding the maximum (largest) value in a finite sequence of integers. 1. Set the tempary maximum equal to the first integer in the sequence. (The tempary maximum will be the largest integer examined at any stage of the procedure.) 2. Compare the next integer in the sequence to the tempary maximum, and if it is larger than the tempary maximum, set the tempary maximum equal to this integer. 3. Repeat the previous step if there are me integers in the sequence. 4. Stop when there are no integers left in the sequence. The tempary maximum at this point is the largest integer in the sequence. 6

2 PSEUDOCODE An algithm can also be described using a computer language A fm of pseudocode is used instead of using a particular computer language like C, C++, Java, etc to specify algithms Pseudocode provides an intermediate step between an English language description of an algithm and an implementation of this algithm in a programming language. PSEUDOCODE In pseudocode, the instructions used can include any well-defined operations statements. A computer program can be produced d in any computer language using the pseudocode description as a starting point. 7 8 COMMON PSEUDOCODE STATEMENTS Procedure Assignment Comments Conditional Constructions Loop Constructions Return 9 Procedure Statements The pseudocode f an algithm begins with a procedure statement that gives the name of an algithm, lists the input variables, and describes what kind of variable each input is. Example: procedure maximum(l: list of integers) is the first statement in the pseudocode description of the algithm, which we have named maximum, that finds the maximum of a list L of integers. 10 Assignment Statements Conditional Constructions used to assign values to variables. symbol := is used the left-hand side is the name of the variable and the right-hand side is an expression that involves constants, variables that have been assigned values, functions defined by procedures and may contain any of the usual arithmetic operations. variable := expression max := a x := largest integer in the list L 11 The simplest fm of the conditional construction that we will use is if condition then statement if condition then block of statements t t if condition then statement 1 else statement 2 If a > b then a = c else a = d 12

3 Loop Constructions f variable := initial value to final value statement f variable := initial value to final value block of statements f i := 1 to n sum := sum +i Loop Constructions while condition statement while condition block of statements while n > 0 sum := sum +n n := n Comments statements enclosed in curly braces are not executed. Such statements serve as comments reminders that help explain how the procedure wks. F instance, the statement {x is the largest element in L} can be used to remind the reader that at that point in the procedure the variable x equals the largest element in the list L. 15 RETURN We use a return statement to show where a procedure produces output. A return statement of the fm return x produces the current value of x as output. The output x can involve the value of one me functions, including the same function under evaluation, but at a smaller value. F instance, the statement return f (n 1) is used to call the algithm with input of n 1. This means that the algithm is run again with input equal to n Example: Finding the Maximum Element in a Finite Sequence. PROPERTIES OF Input. An algithm has input values from a specified set. procedure max(a 1, a 2,..., a n : integers) max : = a 1 f i : = 2 to n if max < a i then max := a i return max {max is the largest element} 17 Output. From each set of input values an algithm produces output values from a specified set. The output values are the solution to the problem. Definiteness. The steps of an algithm must be defined precisely. 18

4 PROPERTIES OF Crectness. An algithm should produce the crect output values f each set of input values. Finiteness. An algithm should produce the desired output after a finite (but perhaps large) number of steps f any input in the set. PROPERTIES OF Effectiveness. It must be possible to perfm each step of an algithm exactly and in a finite amount of time. Generality. The procedure should be applicable f all problems of the desired fm, not just f a particular set of input values USE OF Searching Algithms Sting Greedy Algithms -determine the best choice SAMPLE SEARCHING The Linear Search Algithm procedure linear search(x: integer, a1, a2,..., an: distinct integers) i := 1 while (i n and x = ai ) i := i + 1 if i n then location := i else location := 0 return location{location is the subscript of the term that equals x, is 0 if x is not found} SAMPLE SEARCHING The Binary Search Algithm procedure binary search (x: integer, a1, a2,..., an: increasing integers) i := 1{i is left endpoint of search interval} j := n {j is right endpoint of search interval} while i < j m := (i + j)/2 if x > a m then i := m + 1 else j := m if x = a i then location := i else location := 0 return location {location is the subscript i of the term a i equal to x, 0 if x is not found} 23 SEATWORK Determine which characteristics of an algithm described in the text the following procedures have and which they lack. a) procedure double(n: positive integer) while n > 0 n := 2n 24

5 SEATWORK b) procedure divide(n: positive integer) while n 0 m := 1/n n := n 1 c) procedure sum(n: positive integer) while i < 10 sum := sum + i d) procedure choose(a, b: integers) x := either a b 25

1KOd17RMoURxjn2 CSE 20 DISCRETE MATH Fall

1KOd17RMoURxjn2 CSE 20 DISCRETE MATH Fall CSE 20 https://goo.gl/forms/1o 1KOd17RMoURxjn2 DISCRETE MATH Fall 2017 http://cseweb.ucsd.edu/classes/fa17/cse20-ab/ Today's learning goals Explain the steps in a proof by mathematical and/or structural

More information

Algorithms Lecture 11. UC Davis, ECS20, Winter Discrete Mathematics for Computer Science

Algorithms Lecture 11. UC Davis, ECS20, Winter Discrete Mathematics for Computer Science UC Davis, ECS20, Winter 2017 Discrete Mathematics for Computer Science Prof. Raissa D Souza (slides adopted from Michael Frank and Haluk Bingöl) Lecture 11 Algorithms 3.1-3.2 Algorithms Member of the House

More information

SIMPLIFYING RATIONAL EXPRESSIONS

SIMPLIFYING RATIONAL EXPRESSIONS MATH 65 LAB RATIONAL EXPRESSIONS and EQUATIONS Name Instruct: T. Henson Purpose: Practice in simplifying rational s and in perfming operations with rational s and in solving rational s. To develop understanding

More information

CSE 20 DISCRETE MATH. Fall

CSE 20 DISCRETE MATH. Fall CSE 20 DISCRETE MATH Fall 2017 http://cseweb.ucsd.edu/classes/fa17/cse20-ab/ Final exam The final exam is Saturday December 16 11:30am-2:30pm. Lecture A will take the exam in Lecture B will take the exam

More information

CSE 20 DISCRETE MATH. Winter

CSE 20 DISCRETE MATH. Winter CSE 20 DISCRETE MATH Winter 2017 http://cseweb.ucsd.edu/classes/wi17/cse20-ab/ Final exam The final exam is Saturday March 18 8am-11am. Lecture A will take the exam in GH 242 Lecture B will take the exam

More information

Roundoff Errors and Computer Arithmetic

Roundoff Errors and Computer Arithmetic Jim Lambers Math 105A Summer Session I 2003-04 Lecture 2 Notes These notes correspond to Section 1.2 in the text. Roundoff Errors and Computer Arithmetic In computing the solution to any mathematical problem,

More information

Outline and Reading. Analysis of Algorithms 1

Outline and Reading. Analysis of Algorithms 1 Outline and Reading Algorithms Running time ( 3.1) Pseudo-code ( 3.2) Counting primitive operations ( 3.4) Asymptotic notation ( 3.4.1) Asymptotic analysis ( 3.4.2) Case study ( 3.4.3) Analysis of Algorithms

More information

CSC Discrete Math I, Spring Sets

CSC Discrete Math I, Spring Sets CSC 125 - Discrete Math I, Spring 2017 Sets Sets A set is well-defined, unordered collection of objects The objects in a set are called the elements, or members, of the set A set is said to contain its

More information

Discrete Structures Lecture 14

Discrete Structures Lecture 14 Introduction Instead of closely following our text in chapter 3, we take a different approach based on the industrial experience of your instructor. First, we narrow the application of algorithms to algorithms

More information

MATH 54 - LECTURE 4 DAN CRYTSER

MATH 54 - LECTURE 4 DAN CRYTSER MATH 54 - LECTURE 4 DAN CRYTSER Introduction In this lecture we review properties and examples of bases and subbases. Then we consider ordered sets and the natural order topology that one can lay on an

More information

for (i=1; i<=100000; i++) { x = sqrt (y); // square root function cout << x+i << endl; }

for (i=1; i<=100000; i++) { x = sqrt (y); // square root function cout << x+i << endl; } Ex: The difference between Compiler and Interpreter The interpreter actually carries out the computations specified in the source program. In other words, the output of a compiler is a program, whereas

More information

CSE101: Design and Analysis of Algorithms. Ragesh Jaiswal, CSE, UCSD

CSE101: Design and Analysis of Algorithms. Ragesh Jaiswal, CSE, UCSD Recap. Growth rates: Arrange the following functions in ascending order of growth rate: n 2 log n n log n 2 log n n/ log n n n Introduction Algorithm: A step-by-step way of solving a problem. Design of

More information

CSEN 202 Introduction to Computer Programming

CSEN 202 Introduction to Computer Programming CSEN 202 Introduction to Computer Programming Lecture 4: Iterations Prof. Dr. Slim Abdennadher and Dr Mohammed Abdel Megeed Salem, slim.abdennadher@guc.edu.eg German University Cairo, Department of Media

More information

Outline. Introduction. 2 Proof of Correctness. 3 Final Notes. Precondition P 1 : Inputs include

Outline. Introduction. 2 Proof of Correctness. 3 Final Notes. Precondition P 1 : Inputs include Outline Computer Science 331 Correctness of Algorithms Mike Jacobson Department of Computer Science University of Calgary Lectures #2-4 1 What is a? Applications 2 Recursive Algorithms 3 Final Notes Additional

More information

LECTURE 9 Data Structures: A systematic way of organizing and accessing data. --No single data structure works well for ALL purposes.

LECTURE 9 Data Structures: A systematic way of organizing and accessing data. --No single data structure works well for ALL purposes. LECTURE 9 Data Structures: A systematic way of organizing and accessing data. --No single data structure works well for ALL purposes. Input Algorithm Output An algorithm is a step-by-step procedure for

More information

for (i=1; i<=100000; i++) { x = sqrt (y); // square root function cout << x+i << endl; }

for (i=1; i<=100000; i++) { x = sqrt (y); // square root function cout << x+i << endl; } Ex: The difference between Compiler and Interpreter The interpreter actually carries out the computations specified in the source program. In other words, the output of a compiler is a program, whereas

More information

Searching Algorithms/Time Analysis

Searching Algorithms/Time Analysis Searching Algorithms/Time Analysis CSE21 Fall 2017, Day 8 Oct 16, 2017 https://sites.google.com/a/eng.ucsd.edu/cse21-fall-2017-miles-jones/ (MinSort) loop invariant induction Loop invariant: After the

More information

Number System. Introduction. Natural Numbers (N) Whole Numbers (W) Integers (Z) Prime Numbers (P) Face Value. Place Value

Number System. Introduction. Natural Numbers (N) Whole Numbers (W) Integers (Z) Prime Numbers (P) Face Value. Place Value 1 Number System Introduction In this chapter, we will study about the number system and number line. We will also learn about the four fundamental operations on whole numbers and their properties. Natural

More information

A.1 Numbers, Sets and Arithmetic

A.1 Numbers, Sets and Arithmetic 522 APPENDIX A. MATHEMATICS FOUNDATIONS A.1 Numbers, Sets and Arithmetic Numbers started as a conceptual way to quantify count objects. Later, numbers were used to measure quantities that were extensive,

More information

Lecture 3. Brute Force

Lecture 3. Brute Force Lecture 3 Brute Force 1 Lecture Contents 1. Selection Sort and Bubble Sort 2. Sequential Search and Brute-Force String Matching 3. Closest-Pair and Convex-Hull Problems by Brute Force 4. Exhaustive Search

More information

Beyond Base 10: Non-decimal Based Number Systems

Beyond Base 10: Non-decimal Based Number Systems Beyond Base : Non-decimal Based Number Systems What is the decimal based number system? How do other number systems work (binary, octal and hex) How to convert to and from nondecimal number systems to

More information

Section 8.2 Graph Terminology. Undirected Graphs. Definition: Two vertices u, v in V are adjacent or neighbors if there is an edge e between u and v.

Section 8.2 Graph Terminology. Undirected Graphs. Definition: Two vertices u, v in V are adjacent or neighbors if there is an edge e between u and v. Section 8.2 Graph Terminology Undirected Graphs Definition: Two vertices u, v in V are adjacent or neighbors if there is an edge e between u and v. The edge e connects u and v. The vertices u and v are

More information

University of Toronto Department of Electrical and Computer Engineering. Midterm Examination. ECE 345 Algorithms and Data Structures Fall 2010

University of Toronto Department of Electrical and Computer Engineering. Midterm Examination. ECE 345 Algorithms and Data Structures Fall 2010 University of Toronto Department of Electrical and Computer Engineering Midterm Examination ECE 345 Algorithms and Data Structures Fall 2010 Print your name and ID number neatly in the space provided below;

More information

COMP 352 FALL Tutorial 10

COMP 352 FALL Tutorial 10 1 COMP 352 FALL 2016 Tutorial 10 SESSION OUTLINE Divide-and-Conquer Method Sort Algorithm Properties Quick Overview on Sorting Algorithms Merge Sort Quick Sort Bucket Sort Radix Sort Problem Solving 2

More information

Reals 1. Floating-point numbers and their properties. Pitfalls of numeric computation. Horner's method. Bisection. Newton's method.

Reals 1. Floating-point numbers and their properties. Pitfalls of numeric computation. Horner's method. Bisection. Newton's method. Reals 1 13 Reals Floating-point numbers and their properties. Pitfalls of numeric computation. Horner's method. Bisection. Newton's method. 13.1 Floating-point numbers Real numbers, those declared to be

More information

1 Introduction to generation and random generation

1 Introduction to generation and random generation Contents 1 Introduction to generation and random generation 1 1.1 Features we might want in an exhaustive generation algorithm............... 1 1.2 What about random generation?.................................

More information

Homework #2. If (your ID number s last two digits % 6) = 0: 6, 12, 18

Homework #2. If (your ID number s last two digits % 6) = 0: 6, 12, 18 2005/Sep/19 1 Homework #2 Chapter 1: Exercises 7, 9 with modifications: for Exercise 7.a: 20 and 32 are changed as your ID number s last two digits and 60. for Exercise 9: 47x25 are change as 47x(your

More information

Stanford ProCo 2014 May 18, 2014 Bug 1 Pyramids (page 1 of 1)

Stanford ProCo 2014 May 18, 2014 Bug 1 Pyramids (page 1 of 1) Bug Pyramids (page of ) Print out an ASCII triangle Given a number n, print out an ASCII triangle with n levels using asterisks as the output. The triangle should be n rows tall, with each row having an

More information

C Syntax Out: 15 September, 1995

C Syntax Out: 15 September, 1995 Burt Rosenberg Math 220/317: Programming II/Data Structures 1 C Syntax Out: 15 September, 1995 Constants. Integer such as 1, 0, 14, 0x0A. Characters such as A, B, \0. Strings such as "Hello World!\n",

More information

JME Language Reference Manual

JME Language Reference Manual JME Language Reference Manual 1 Introduction JME (pronounced jay+me) is a lightweight language that allows programmers to easily perform statistic computations on tabular data as part of data analysis.

More information

Time Analysis of Sorting and Searching Algorithms

Time Analysis of Sorting and Searching Algorithms Time Analysis of Sorting and Searching Algorithms CSE21 Winter 2017, Day 5 (B00), Day 3 (A00) January 20, 2017 http://vlsicad.ucsd.edu/courses/cse21-w17 Binary Search: WHEN procedure binary search (x:

More information

Output: For each size provided as input, a figure of that size is to appear, followed by a blank line.

Output: For each size provided as input, a figure of that size is to appear, followed by a blank line. Problem 1: Divisor Differences Develop a program that, given integers m and k satisfying m > k > 0, lists every pair of positive integers (i,j) such that j i = k and both i and j are divisors of m. Input:

More information

Lecture 2: Sorting and the Big O. Wednesday, 16 September 2009

Lecture 2: Sorting and the Big O. Wednesday, 16 September 2009 Lecture 2: Sorting and the Big O CS204/209 : Algorithms (and Scientific Computing) Niall Madden Wednesday, 16 September 2009 CS204/209 Lecture 2: Sorting and the Big O 1/18 In today s lecture 1 Recall...

More information

Organisation. Assessment

Organisation. Assessment Week 1 s s Getting Started 1 3 4 5 - - Lecturer Dr Lectures Tuesday 1-13 Fulton House Lecture room Tuesday 15-16 Fulton House Lecture room Thursday 11-1 Fulton House Lecture room Friday 10-11 Glyndwr C

More information

In Java, data type boolean is used to represent Boolean data. Each boolean constant or variable can contain one of two values: true or false.

In Java, data type boolean is used to represent Boolean data. Each boolean constant or variable can contain one of two values: true or false. CS101, Mock Boolean Conditions, If-Then Boolean Expressions and Conditions The physical order of a program is the order in which the statements are listed. The logical order of a program is the order in

More information

Beyond Base 10: Non-decimal Based Number Systems

Beyond Base 10: Non-decimal Based Number Systems Beyond Base : Non-decimal Based Number Systems What is the decimal based number system? How do other number systems work (binary, octal and hex) How to convert to and from nondecimal number systems to

More information

CSE 20 DISCRETE MATH WINTER

CSE 20 DISCRETE MATH WINTER CSE 20 DISCRETE MATH WINTER 2016 http://cseweb.ucsd.edu/classes/wi16/cse20-ab/ Today's learning goals Explain the steps in a proof by (strong) mathematical induction Use (strong) mathematical induction

More information

Introduction to Computer Science Unit 2. Notes

Introduction to Computer Science Unit 2. Notes Introduction to Computer Science Unit 2. Notes Name: Objectives: By the completion of this packet, students should be able to describe the difference between.java and.class files and the JVM. create and

More information

Stanford University CS261: Optimization Handout 1 Luca Trevisan January 4, 2011

Stanford University CS261: Optimization Handout 1 Luca Trevisan January 4, 2011 Stanford University CS261: Optimization Handout 1 Luca Trevisan January 4, 2011 Lecture 1 In which we describe what this course is about and give two simple examples of approximation algorithms 1 Overview

More information

Set4. 8 February 2019 OSU CSE 1

Set4. 8 February 2019 OSU CSE 1 Set4 8 February 2019 OSU CSE 1 Documenting Set4 (and Map4) By now you understand hashing and its benefits. But the algorithms we are using are not trivial, and make a lot of assumptions about the representation.

More information

Variables and Data Representation

Variables and Data Representation You will recall that a computer program is a set of instructions that tell a computer how to transform a given set of input into a specific output. Any program, procedural, event driven or object oriented

More information

1-1 Sets of Numbers. Warm Up Lesson Presentation Lesson Quiz. Holt Algebra 2

1-1 Sets of Numbers. Warm Up Lesson Presentation Lesson Quiz. Holt Algebra 2 1-1 Sets of Numbers Warm Up Lesson Presentation Lesson Quiz Warm Up Write in decimal form. 1. 4.5 2. 3. Write as a decimal approximation. 1.414 Order from least to greatest. 4. 10, 5, 10, 0, 5 10, 5, 0,

More information

2 Sets. 2.1 Notation. last edited January 26, 2016

2 Sets. 2.1 Notation. last edited January 26, 2016 2 Sets Sets show up in virtually every topic in mathematics, and so understanding their basics is a necessity for understanding advanced mathematics. As far as we re concerned, the word set means what

More information

Elementary maths for GMT. Algorithm analysis Part I

Elementary maths for GMT. Algorithm analysis Part I Elementary maths for GMT Algorithm analysis Part I Algorithms An algorithm is a step-by-step procedure for solving a problem in a finite amount of time Most algorithms transform input objects into output

More information

Introduction to Programming in C Department of Computer Science and Engineering. Lecture No. #43. Multidimensional Arrays

Introduction to Programming in C Department of Computer Science and Engineering. Lecture No. #43. Multidimensional Arrays Introduction to Programming in C Department of Computer Science and Engineering Lecture No. #43 Multidimensional Arrays In this video will look at multi-dimensional arrays. (Refer Slide Time: 00:03) In

More information

Introduction to Object-Oriented Programming

Introduction to Object-Oriented Programming Introduction to Object-Oriented Programming Conditional Execution Christopher Simpkins chris.simpkins@gatech.edu CS 1331 (Georgia Tech) Conditional Execution 1 / 14 Structured Programming In reasoning

More information

Objective. Vocabulary. 1.1: Sets of Numbers. 1-1 Sets of Numbers

Objective. Vocabulary. 1.1: Sets of Numbers. 1-1 Sets of Numbers Starter 1.1 Write in decimal form. 1. 4.5 2. 3. Write as a decimal approximation. 1.414 Order from least to greatest. 4. 10, 5, 10, 0, 5 10, 5, 0, 5, 10 5. 0.1, 1, 1.1, 0.01, 0.11, 0.009 0.009, 0.01, 0.1,

More information

CS228 - Basic Counting and the Pigeonhole Principle

CS228 - Basic Counting and the Pigeonhole Principle CS228 - Basic Counting and the Pigeonhole Principle Nathan Sprague February 19, 2014 Material in these slides is from Discrete Mathematics and Its Applications 7e, Kenneth Rosen, 2012. The Product Rule

More information

SET DEFINITION 1 elements members

SET DEFINITION 1 elements members SETS SET DEFINITION 1 Unordered collection of objects, called elements or members of the set. Said to contain its elements. We write a A to denote that a is an element of the set A. The notation a A denotes

More information

Solutions to Problem 1 of Homework 3 (10 (+6) Points)

Solutions to Problem 1 of Homework 3 (10 (+6) Points) Solutions to Problem 1 of Homework 3 (10 (+6) Points) Sometimes, computing extra information can lead to more efficient divide-and-conquer algorithms. As an example, we will improve on the solution to

More information

1 Finite Representations of Languages

1 Finite Representations of Languages 1 Finite Representations of Languages Languages may be infinite sets of strings. We need a finite notation for them. There are at least four ways to do this: 1. Language generators. The language can be

More information

Taibah University College of Computer Science & Engineering Course Title: Discrete Mathematics Code: CS 103. Chapter 2. Sets

Taibah University College of Computer Science & Engineering Course Title: Discrete Mathematics Code: CS 103. Chapter 2. Sets Taibah University College of Computer Science & Engineering Course Title: Discrete Mathematics Code: CS 103 Chapter 2 Sets Slides are adopted from Discrete Mathematics and It's Applications Kenneth H.

More information

CMSC 451: Lecture 10 Dynamic Programming: Weighted Interval Scheduling Tuesday, Oct 3, 2017

CMSC 451: Lecture 10 Dynamic Programming: Weighted Interval Scheduling Tuesday, Oct 3, 2017 CMSC 45 CMSC 45: Lecture Dynamic Programming: Weighted Interval Scheduling Tuesday, Oct, Reading: Section. in KT. Dynamic Programming: In this lecture we begin our coverage of an important algorithm design

More information

Working with JavaScript

Working with JavaScript Working with JavaScript Creating a Programmable Web Page for North Pole Novelties 1 Objectives Introducing JavaScript Inserting JavaScript into a Web Page File Writing Output to the Web Page 2 Objectives

More information

Math 3336 Section 6.1 The Basics of Counting The Product Rule The Sum Rule The Subtraction Rule The Division Rule

Math 3336 Section 6.1 The Basics of Counting The Product Rule The Sum Rule The Subtraction Rule The Division Rule Math 3336 Section 6.1 The Basics of Counting The Product Rule The Sum Rule The Subtraction Rule The Division Rule Examples, Examples, and Examples Tree Diagrams Basic Counting Principles: The Product Rule

More information

The Size of the Cantor Set

The Size of the Cantor Set The Size of the Cantor Set Washington University Math Circle November 6, 2016 In mathematics, a set is a collection of things called elements. For example, {1, 2, 3, 4}, {a,b,c,...,z}, and {cat, dog, chicken}

More information

CS112 Lecture: Repetition Statements

CS112 Lecture: Repetition Statements CS112 Lecture: Repetition Statements Objectives: Last revised 2/18/05 1. To explain the general form of the java while loop 2. To introduce and motivate the java do.. while loop 3. To explain the general

More information

Implementing Keychain Management oncisco IOS XR Software

Implementing Keychain Management oncisco IOS XR Software Implementing Keychain Management oncisco IOS XR Software This module describes how to implement keychain management on Cisco IOS XR software. Keychain management is a common method of authentication to

More information

Floating-point representation

Floating-point representation Lecture 3-4: Floating-point representation and arithmetic Floating-point representation The notion of real numbers in mathematics is convenient for hand computations and formula manipulations. However,

More information

A SIMPLE APPROXIMATION ALGORITHM FOR NONOVERLAPPING LOCAL ALIGNMENTS (WEIGHTED INDEPENDENT SETS OF AXIS PARALLEL RECTANGLES)

A SIMPLE APPROXIMATION ALGORITHM FOR NONOVERLAPPING LOCAL ALIGNMENTS (WEIGHTED INDEPENDENT SETS OF AXIS PARALLEL RECTANGLES) Chapter 1 A SIMPLE APPROXIMATION ALGORITHM FOR NONOVERLAPPING LOCAL ALIGNMENTS (WEIGHTED INDEPENDENT SETS OF AXIS PARALLEL RECTANGLES) Piotr Berman Department of Computer Science & Engineering Pennsylvania

More information

2 Review of Set Theory

2 Review of Set Theory 2 Review of Set Theory Example 2.1. Let Ω = {1, 2, 3, 4, 5, 6} 2.2. Venn diagram is very useful in set theory. It is often used to portray relationships between sets. Many identities can be read out simply

More information

Subset sum problem and dynamic programming

Subset sum problem and dynamic programming Lecture Notes: Dynamic programming We will discuss the subset sum problem (introduced last time), and introduce the main idea of dynamic programming. We illustrate it further using a variant of the so-called

More information

(Not Quite) Minijava

(Not Quite) Minijava (Not Quite) Minijava CMCS22620, Spring 2004 April 5, 2004 1 Syntax program mainclass classdecl mainclass class identifier { public static void main ( String [] identifier ) block } classdecl class identifier

More information

2 Computation with Floating-Point Numbers

2 Computation with Floating-Point Numbers 2 Computation with Floating-Point Numbers 2.1 Floating-Point Representation The notion of real numbers in mathematics is convenient for hand computations and formula manipulations. However, real numbers

More information

Lecture 1. 1 Notation

Lecture 1. 1 Notation Lecture 1 (The material on mathematical logic is covered in the textbook starting with Chapter 5; however, for the first few lectures, I will be providing some required background topics and will not be

More information

Algorithms. Notations/pseudo-codes vs programs Algorithms for simple problems Analysis of algorithms

Algorithms. Notations/pseudo-codes vs programs Algorithms for simple problems Analysis of algorithms Algorithms Notations/pseudo-codes vs programs Algorithms for simple problems Analysis of algorithms Is it correct? Loop invariants Is it good? Efficiency Is there a better algorithm? Lower bounds * DISC

More information

COMS 1003 Fall Introduction to Computer Programming in C. Bits, Boolean Logic & Discrete Math. September 13 th

COMS 1003 Fall Introduction to Computer Programming in C. Bits, Boolean Logic & Discrete Math. September 13 th COMS 1003 Fall 2005 Introduction to Computer Programming in C Bits, Boolean Logic & Discrete Math September 13 th Hello World! Logistics See the website: http://www.cs.columbia.edu/~locasto/ Course Web

More information

Operators. Java operators are classified into three categories:

Operators. Java operators are classified into three categories: Operators Operators are symbols that perform arithmetic and logical operations on operands and provide a meaningful result. Operands are data values (variables or constants) which are involved in operations.

More information

Imelda C. Go, South Carolina Department of Education, Columbia, SC

Imelda C. Go, South Carolina Department of Education, Columbia, SC PO 082 Rounding in SAS : Preventing Numeric Representation Problems Imelda C. Go, South Carolina Department of Education, Columbia, SC ABSTRACT As SAS programmers, we come from a variety of backgrounds.

More information

COS 226 Algorithms and Data Structures Fall Midterm

COS 226 Algorithms and Data Structures Fall Midterm COS 226 Algorithms and Data Structures Fall 2017 Midterm This exam has 10 questions (including question 0) worth a total of 55 points. You have 0 minutes. This exam is preprocessed by a computer, so please

More information

From the Grade 8, High School, Functions Progression Document, pp. 7-8:

From the Grade 8, High School, Functions Progression Document, pp. 7-8: Time Frame: Approximately 2-3 weeks Connections to Previous Learning: Students use their knowledge of expressions and equations to evaluate functions and to rewrite when necessary. They also use rigid

More information

Computational Methods CMSC/AMSC/MAPL 460. Representing numbers in floating point and associated issues. Ramani Duraiswami, Dept. of Computer Science

Computational Methods CMSC/AMSC/MAPL 460. Representing numbers in floating point and associated issues. Ramani Duraiswami, Dept. of Computer Science Computational Methods CMSC/AMSC/MAPL 460 Representing numbers in floating point and associated issues Ramani Duraiswami, Dept. of Computer Science Class Outline Computations should be as accurate and as

More information

CS102 Unit 2. Sets and Mathematical Formalism Programming Languages and Simple Program Execution

CS102 Unit 2. Sets and Mathematical Formalism Programming Languages and Simple Program Execution 1 CS102 Unit 2 Sets and Mathematical Formalism Programming Languages and Simple Program Execution 2 Review Show how "Hi!\n" would be stored in the memory below Use decimal to represent each byte Remember

More information

Primitive Data, Variables, and Expressions; Simple Conditional Execution

Primitive Data, Variables, and Expressions; Simple Conditional Execution Unit 2, Part 1 Primitive Data, Variables, and Expressions; Simple Conditional Execution Computer Science S-111 Harvard University David G. Sullivan, Ph.D. Overview of the Programming Process Analysis/Specification

More information

Administration Computers Software Algorithms Programming Languages

Administration Computers Software Algorithms Programming Languages Administration Computers Software Algorithms Programming Languages http://www.cs.sfu.ca/cc/130/johnwill/ This course does not use Canvas John Edgar 3 Come to class Read ahead Attend the labs You get practice

More information

1. (15 points) Solve the decanting problem for containers of sizes 199 and 179; that is find integers x and y satisfying.

1. (15 points) Solve the decanting problem for containers of sizes 199 and 179; that is find integers x and y satisfying. May 9, 2003 Show all work Name There are 260 points available on this test 1 (15 points) Solve the decanting problem for containers of sizes 199 and 179; that is find integers x and y satisfying where

More information

MATH HOMEWORK 2 1

MATH HOMEWORK 2 1 MATH 220 - HOMEWORK 2 1 Problem (1.40). For i Z, let A i = {i 1,i+1}. Determine the following: (a) (b) (c) A 2i (A i A i+1 ) (A 2i 1 A 2i+1 ) Solution. (a) Since A 2i = {2i 1,2i+1} and A 2i = A 2 A 4 A

More information

Lecture Programming in C++ PART 1. By Assistant Professor Dr. Ali Kattan

Lecture Programming in C++ PART 1. By Assistant Professor Dr. Ali Kattan Lecture 08-1 Programming in C++ PART 1 By Assistant Professor Dr. Ali Kattan 1 The Conditional Operator The conditional operator is similar to the if..else statement but has a shorter format. This is useful

More information

Hardware versus software

Hardware versus software Logic 1 Hardware versus software 2 In hardware such as chip design or architecture, designs are usually proven to be correct using proof tools In software, a program is very rarely proved correct Why?

More information

Department of Computer Science COMP The Programming Competency Test

Department of Computer Science COMP The Programming Competency Test The Australian National University Faculty of Engineering & Information Technology Department of Computer Science COMP1120-2003-01 The Programming Competency Test 1 Introduction The purpose of COMP1120

More information

CS 261 Fall Floating-Point Numbers. Mike Lam, Professor.

CS 261 Fall Floating-Point Numbers. Mike Lam, Professor. CS 261 Fall 2018 Mike Lam, Professor https://xkcd.com/217/ Floating-Point Numbers Floating-point Topics Binary fractions Floating-point representation Conversions and rounding error Binary fractions Now

More information

Discrete Structures Lecture The Basics of Counting

Discrete Structures Lecture The Basics of Counting Introduction Good morning. Combinatorics is the study of arrangements of objects. Perhaps, the first application of the study of combinatorics was in the study of gambling games. Understanding combinatorics

More information

Randomly Generated Numerical Semigroups

Randomly Generated Numerical Semigroups Randomly Generated Numerical Semigroups By Zachary J. Spaulding Abstract. Numerical semigroups are subsemigroups of N, that is, subsets of the natural numbers closed under addition. In this paper, we will

More information

Computer System and programming in C

Computer System and programming in C Approaches to Problem Solving Concept of algorithm and flow charts ALGORITHMS AND FLOWCHARTS A typical programming task can be divided into two phases: Problem solving phase produce an ordered sequence

More information

Semantics via Syntax. f (4) = if define f (x) =2 x + 55.

Semantics via Syntax. f (4) = if define f (x) =2 x + 55. 1 Semantics via Syntax The specification of a programming language starts with its syntax. As every programmer knows, the syntax of a language comes in the shape of a variant of a BNF (Backus-Naur Form)

More information

University of Toronto Department of Electrical and Computer Engineering. Midterm Examination. ECE 345 Algorithms and Data Structures Fall 2012

University of Toronto Department of Electrical and Computer Engineering. Midterm Examination. ECE 345 Algorithms and Data Structures Fall 2012 1 University of Toronto Department of Electrical and Computer Engineering Midterm Examination ECE 345 Algorithms and Data Structures Fall 2012 Print your name and ID number neatly in the space provided

More information

COMP 102: Computers and Computing

COMP 102: Computers and Computing COMP 102: Computers and Computing Lecture 5: What is Programming? Instructor: Kaleem Siddiqi (siddiqi@cim.mcgill.ca) Class web page: www.cim.mcgill.ca/~siddiqi/102.html Motivation The advantage of a computer

More information

CS 112 Introduction to Computing II. Wayne Snyder Computer Science Department Boston University

CS 112 Introduction to Computing II. Wayne Snyder Computer Science Department Boston University CS 112 Introduction to Computing II Wayne Snyder Department Boston University Today: Java basics: Compilation vs Interpretation Program structure Statements Values Variables Types Operators and Expressions

More information

CS 483. Jana Kosecka CS Dept Eng. Building

CS 483. Jana Kosecka CS Dept Eng. Building CS 483 Jana Kosecka CS Dept. 4444 Eng. Building kosecka@gmu.edu Course Info Course webpage: from the syllabus on http://cs.gmu.edu/courses/ Information you will find course syllabus, time table office

More information

SME1013 PROGRAMMING FOR ENGINEERS

SME1013 PROGRAMMING FOR ENGINEERS SME1013 PROGRAMMING FOR ENGINEERS Ainullotfi bin Abdul Latif Faculty of Mechanical Engineering UTM Problem Solving Recognise and understand the problem (what is it that needed to be solved?) List the parameters

More information

18 Spanning Tree Algorithms

18 Spanning Tree Algorithms November 14, 2017 18 Spanning Tree Algorithms William T. Trotter trotter@math.gatech.edu A Networking Problem Problem The vertices represent 8 regional data centers which need to be connected with high-speed

More information

2 Computation with Floating-Point Numbers

2 Computation with Floating-Point Numbers 2 Computation with Floating-Point Numbers 2.1 Floating-Point Representation The notion of real numbers in mathematics is convenient for hand computations and formula manipulations. However, real numbers

More information

Chapter 2. The Algorithmic Foundations of. Computer Science INVITATION TO. Computer Science. Tuesday, September 10, 13

Chapter 2. The Algorithmic Foundations of. Computer Science INVITATION TO. Computer Science. Tuesday, September 10, 13 Chapter 2 The Algorithmic Foundations of Computer Science INVITATION TO Computer Science 1 Objectives After studying this chapter, students will be able to: Explain the benefits of pseudocode over natural

More information

Only to be used for arranged hours. Order of Operations

Only to be used for arranged hours. Order of Operations Math 84 Activity # 1 Your name: Order of Operations Goals: 1) Evaluate Real numbers with Exponents. ) Use the Order of Operations to Evaluate Expressions. ) Review Exponents and Powers of Ten Integer exponents

More information

An Evolution of Mathematical Tools

An Evolution of Mathematical Tools An Evolution of Mathematical Tools From Conceptualization to Formalization Here's what we do when we build a formal model (or do a computation): 0. Identify a collection of objects/events in the real world.

More information

Linear Sorts. EECS 2011 Prof. J. Elder - 1 -

Linear Sorts. EECS 2011 Prof. J. Elder - 1 - Linear Sorts - - Linear Sorts? Comparison sorts are very general, but are W( nlog n) Faster sorting may be possible if we can constrain the nature of the input. - - Ø Counting Sort Ø Radix Sort Ø Bucket

More information

9. MATHEMATICIANS ARE FOND OF COLLECTIONS

9. MATHEMATICIANS ARE FOND OF COLLECTIONS get the complete book: http://wwwonemathematicalcatorg/getfulltextfullbookhtm 9 MATHEMATICIANS ARE FOND OF COLLECTIONS collections Collections are extremely important in life: when we group together objects

More information

CSC 373 Lecture # 3 Instructor: Milad Eftekhar

CSC 373 Lecture # 3 Instructor: Milad Eftekhar Huffman encoding: Assume a context is available (a document, a signal, etc.). These contexts are formed by some symbols (words in a document, discrete samples from a signal, etc). Each symbols s i is occurred

More information

APCS Semester #1 Final Exam Practice Problems

APCS Semester #1 Final Exam Practice Problems Name: Date: Per: AP Computer Science, Mr. Ferraro APCS Semester #1 Final Exam Practice Problems The problems here are to get you thinking about topics we ve visited thus far in preparation for the semester

More information

1. Suppose you are given a magic black box that somehow answers the following decision problem in polynomial time:

1. Suppose you are given a magic black box that somehow answers the following decision problem in polynomial time: 1. Suppose you are given a magic black box that somehow answers the following decision problem in polynomial time: Input: A CNF formula ϕ with n variables x 1, x 2,..., x n. Output: True if there is an

More information