Test Bank for Introduction to Algorithms and Data Structures, 1st Edition by Perez (All Chapters included)
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Module
Algorithms
Institution
Algorithms
Complete Test Bank for Introduction to Algorithms and Data Structures, 1st Edition by Perez ; ISBN13: 9780357673560....(Full Chapters included and organized in reverse order from Chapter 10 to 1)...CHAPTER 1. RECURSION
CHAPTER 2. INTRODUCTION TO DATA STRUCTURES
CHAPTER 3. DESIGNING EFFICIENT ALGO...
Introduction to Algorithms and Data
Structures, 1st Edition by Perez
Complete Chapters Test Bank
are included (Ch 1 to 10)
** Immediate Download
** Swift Response
** All Chapters included
,Table of Contents are given below
CHAPTER 1. RECURSION
CHAPTER 2. INTRODUCTION TO DATA
STRUCTURES
CHAPTER 3. DESIGNING EFFICIENT ALGORITHMS
CHAPTER 4. SORTING ALGORITHMS
CHAPTER 5. SEARCH ALGORITHMS
CHAPTER 6. LINKED LISTS, STACKS, AND QUEUES
CHAPTER 7. HASH TABLES
CHAPTER 8. TREES
CHAPTER 9. GRAPHS
CHAPTER 10. ADVANCED ALGORITHMS
, The test bank is organized in reverse order, with the last chapter displayed first, to ensure that all chapters
are included in this document. (Complete Chapters included Ch10-1)
Chapter 10 - Advanced Algorithms
1. A greedy algorithm divides a problem into subproblems and builds a feasible solution for each subproblem that
maximizes or minimizes the objective function.
a. True
b. False
ANSWER: True
FEEDBACK: Correct An optimization problem maximizes or minimizes a quantity, subject to certain
conditions. The objective function is the quantity to maximize or minimize. A greedy
algorithm divides a problem into subproblems and builds a feasible solution for
each subproblem that maximizes or minimizes the objective function.
Incorrect An optimization problem maximizes or minimizes a quantity, subject to certain
conditions. The objective function is the quantity to maximize or minimize. A greedy
algorithm divides a problem into subproblems and builds a feasible solution for
each subproblem that maximizes or minimizes the objective function.
POINTS: 1
DIFFICULTY: Introductory
REFERENCES: Greedy Algorithms
QUESTION TYPE: True / False
HAS VARIABLES: False
LEARNING OBJECTIVES: IADS.CENG.24.10.1.1 - Describe the principle behind greedy algorithms.
KEYWORDS: Bloom's: Understand
DATE CREATED: 9/11/2023 2:11 PM
DATE MODIFIED: 9/11/2023 2:45 PM
2. What is required for a greedy algorithm to be successful?
a. greedy choice property and optimal sub-structure
b. data compression and variable-length codification
c. Huffman coding and frequency histogram
d. recursion and memoization
ANSWER: a
FEEDBACK: a. Correct. For a greedy algorithm to succeed—that is, to reach a globally optimal
solution—the problem must have two properties: 1) the greedy choice property,
which means that taking the locally optimal solution at each step of the
problem—that is, taking the local best choice—would lead to a globally optimal
solution; and 2) an optimal sub-structure, which means that an optimal solution is
composed of optimal solutions to its subproblems.
b. Incorrect. For a greedy algorithm to succeed—that is, to reach a globally optimal
solution—the problem must have two properties: 1) the greedy choice property,
which means that taking the locally optimal solution at each step of the
problem—that is, taking the local best choice—would lead to a globally optimal
solution; and 2) an optimal sub-structure, which means that an optimal solution is
composed of optimal solutions to its subproblems. Data compression is one
application of greedy algorithms and refers to building a new representation of
information that requires less space but conveys the same information. Variable-
length codification is one type of data compression that uses a representation of
characters from 6 to 9, which is optimal.
c. Incorrect. For a greedy algorithm to succeed—that is, to reach a globally optimal
solution—the problem must have two properties: 1) the greedy choice property,
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, Name: Class: Date:
Chapter 10 - Advanced Algorithms
which means that taking the locally optimal solution at each step of the
problem—that is, taking the local best choice—would lead to a globally optimal
solution; and 2) an optimal sub-structure, which means that an optimal solution is
composed of optimal solutions to its subproblems. Huffman coding is one
application of greedy algorithms. It uses a greedy strategy to build a codification
for the characters of a file in such a way that characters with a higher frequency
use the smallest possible code. A frequency histogram is used in Huffman coding
and records how many times a character is repeated in a text.
d. Incorrect. For a greedy algorithm to succeed—that is, to reach a globally optimal
solution—the problem must have two properties: 1) the greedy choice property,
which means that taking the locally optimal solution at each step of the
problem—that is, taking the local best choice—would lead to a globally optimal
solution; and 2) an optimal sub-structure, which means that an optimal solution is
composed of optimal solutions to its subproblems. Recursion and memoization
are the components of dynamic programming strategies, not greedy algorithms.
POINTS: 1
DIFFICULTY: Intermediate
REFERENCES: Greedy Algorithms
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
LEARNING OBJECTIVES: IADS.CENG.24.10.1.2 - List the parts of a greedy algorithm.
KEYWORDS: Bloom's: Remember
DATE CREATED: 9/11/2023 2:13 PM
DATE MODIFIED: 9/11/2023 2:45 PM
3. Which characters are used in the initial step to construct a Huffman code?
a. the last two characters in the text
b. the first two characters in the text
c. the two characters with the least frequency
d. the two characters with the greatest frequency
ANSWER: c
FEEDBACK: a. Incorrect. Constructing a Huffman code starts by building a binary tree where the
root is a node associated with the Null value and a value equal to the sum of the
frequencies of the two characters with the least frequency, not the last two
characters in the text. Huffman coding is organized around a frequency table, so
the relative frequencies of the characters are used rather than their position in the
text.
b. Incorrect. Constructing a Huffman code starts by building a binary tree where the
root is a node associated with the Null value and a value equal to the sum of the
frequencies of the two characters with the least frequency, not the first two
characters in the text. Huffman coding is organized around a frequency table, so
the relative frequencies of the characters are used rather than their position in the
text.
c. Correct. Constructing a Huffman code starts by building a binary tree where the
root is a node associated with the Null value and a value equal to the sum of the
frequencies of the two characters with the least frequency.
d. Incorrect. Constructing a Huffman code starts by building a binary tree where the
root is a node associated with the Null value and a value equal to the sum of the
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