Chapter 2. The Algorithmic Foundations of. Computer Science INVITATION TO. Computer Science. Tuesday, September 10, 13
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1 Chapter 2 The Algorithmic Foundations of Computer Science INVITATION TO Computer Science 1
2 Objectives After studying this chapter, students will be able to: Explain the benefits of pseudocode over natural language or a programming language Represent algorithms using pseudocode Identify algorithm statements as sequential, conditional, or iterative Define abstraction and top-down design, and explain their use in breaking down complex problems Invitation to Computer Science, 6th Edition 2
3 Objectives (continued) After studying this chapter, students will be able to: Illustrate the operation of sample algorithms multiplication by repeated addition sequential search of a collection of values finding the maximum element in a collection finding a pattern string in a larger piece of text Invitation to Computer Science, 6th Edition 3
4 Introduction Algorithms for everyday may not be suitable for computers to perform (as in Chapter 1) Algorithmic problem solving focuses on algorithms suitable for computers Pseudocode is a tool for designing algorithms This chapter will use a set of problems to illustrate algorithmic problem solving Invitation to Computer Science, 6th Edition 4
5 Representing Algorithms Pseudocode is used to design algorithms Natural language is: expressive, easy to use verbose, unstructured, and ambiguous Programming languages are: structured, designed for computers grammatically fussy, cryptic Pseudocode lies somewhere between these two Invitation to Computer Science, 6th Edition 5
6 Invitation to Computer Science, 6th Edition 6
7 Invitation to Computer Science, 6th Edition 7
8 Representing Algorithms (continued) Sequential operations perform a single task Computation: a single numeric calculation Input: gets data values from outside the algorithm Output: sends data values to the outside world A variable is a named location to hold a value A sequential algorithm is made up only of sequential operations Example: computing average miles per gallon Invitation to Computer Science, 6th Edition 8
9 Invitation to Computer Science, 6th Edition 9
10 Representing Algorithms (continued) Control operation: changes the normal flow of control Conditional statement: asks a question and selects among alternative options 1. Evaluate the true/false condition 2. If the condition is true, then do the first set of operations and skip the second set 3. If the condition is false, skip the first set of operations and do the second set Example: check for good or bad gas mileage Invitation to Computer Science, 6th Edition 10
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12 Invitation to Computer Science, 6th Edition 12
13 Representing Algorithms (continued) Iteration: an operation that causes looping, repeating a block of instructions While statement repeats while a condition remains true continuation condition: a test to see if while loop should continue loop body: instructions to perform repeatedly Example: repeated mileage calculations Invitation to Computer Science, 6th Edition 13
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16 Representing Algorithms (continued) Do/while, alternate iterative operation continuation condition appears at the end loop body always performed at least onc Primitive operations: sequential, conditional, and iterative are all that is needed Invitation to Computer Science, 6th Edition 16
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19 Examples of Algorithmic Problem Solving Example 1: Go Forth and Multiply Given two nonnegative integer values, a 0, b 0, compute and output the product (a b) using the technique of repeated addition. That is, determine the value of the sum a + a + a a (b times). Invitation to Computer Science, 6th Edition 19
20 Examples of Algorithmic Problem Solving Get input values Get values for a and b Compute the answer Loop b times, adding each time* Output the result Print the final value* * steps need elaboration Invitation to Computer Science, 6th Edition 20
21 Examples of Algorithmic Problem Solving Example 1: Go Forth and Multiply (continued) Loop b times, adding each time Set the value of count to 0 While (count < b) do the rest of the loop* Set the value of count to count + 1 End of loop * steps need elaboration Invitation to Computer Science, 6th Edition 21
22 Examples of Algorithmic Problem Solving Loop b times, adding each time Set the value of count to 0 Set the value of product to 0 While (count < b) do Set the value of product to (product + a) Set the value of count to count + 1 End of loop Output the result Print the value of product Invitation to Computer Science, 6th Edition 22
23 Invitation to Computer Science, 6th Edition 23
24 Examples of Algorithmic Problem Solving Example 2: Looking, Looking, Looking Assume that we have a list of 10,000 names that we define as N 1, N 2, N 3,..., N 10,000, along with the 10,000 telephone numbers of those individuals, denoted as T 1, T 2, T 3,..., T 10,000. To simplify the problem, we initially assume that all names in the book are unique and that the names need not be in alphabetical order. Invitation to Computer Science, 6th Edition 24
25 Examples of Algorithmic Problem Solving Example 2: Looking, Looking, Looking (continued) Three versions here illustrate algorithm discovery, working toward a correct, efficient solution A sequential algorithm (no loops or conditionals) An incomplete iterative algorithm A correct algorithm Invitation to Computer Science, 6th Edition 25
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28 Invitation to Computer Science, 6th Edition 28
29 Examples of Algorithmic Problem Solving Example 3: Big, Bigger, Biggest A building-block algorithm used in many libraries Library: A collection of pre-defined useful algorithms Given a value n 1 and a list containing exactly n unique numbers called A 1, A 2,..., A n, find and print out both the largest value in the list and the position in the list where that largest value occurred. Invitation to Computer Science, 6th Edition 29
30 Invitation to Computer Science, 6th Edition 30
31 Examples of Algorithmic Problem Solving Pattern-matching: common across many applications word processor search, web search, image analysis, human genome project You will be given some text composed of n characters that will be referred to as T 1 T 2... T n. You will also be given a pattern of m characters, m n, that will be represented as P 1 P 2... P m. The algorithm must locate every occurrence of the given pattern within the text. The output of the algorithm is the location in the text where each match occurred. Invitation to Computer Science, 6th Edition 31
32 Examples of Algorithmic Problem Solving Example 4: Meeting Your Match (continued) Algorithm has two parts: 1. Sliding the pattern along the text, aligning it with each position in turn 2. Given a particular alignment, determine if there is a match at that location Solve parts separately and use Abstraction, focus on high level, not details Top-down design, start with big picture, gradually elaborate parts Invitation to Computer Science, 6th Edition 32
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35 Summary Pseudocode is used for algorithm design: structured like code, but allows English and math phrasing and notation Pseudocode is made up of: sequential, conditional, and iterative operations Algorithmic problem solving involves: Step-by-step development of algorithm pieces Use of abstraction, and top-down design Invitation to Computer Science, 6th Edition 35
9/23/2013. Chapter 2. Objectives. Introduction. Objectives (continued) Representing Algorithms. Representing Algorithms
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