Introduction to the Design and Analysis of Algorithms 2nd Edition by Anany Levitin 8131718379 9788131718377

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Product details:

ISBN 10: 8131718379

ISBN 13: 9788131718377

Author: Anany Levitin

Based on a new classification of algorithm design techniques and a clear delineation of analysis methods, “Introduction to the Design and Analysis of Algorithms” presents the subject in a coherent and innovative manner. Written in a student-friendly style, the book emphasizes the understanding of ideas over excessively formal treatment while thoroughly covering the material required in an introductory algorithms course. Popular puzzles are used to motivate students’ interest and strengthen their skills in algorithmic problem solving. Other learning-enhancement features include chapter summaries, hints to the exercises and a detailed solution manual.

Table of contents:

Chapter 1. Introduction
1.1 What is an Algorithm?
1.2 Fundamentals of Algorithmic Problem Solving
1.3 Important Problem Types
1.4 Fundamental Data Structures

Chapter 2. Fundamentals of the Analysis of Algorithm Efficiency
2.1 Analysis Framework
2.2 Asymptotic Notations and Basic Efficiency Classes
2.3 Mathematical Analysis of Nonrecursive Algorithms
2.4 Mathematical Analysis of Recursive Algorithms
2.5 Example: Fibonacci Numbers
2.6 Empirical Analysis of Algorithms
2.7 Algorithm Visualization

Chapter 3. Brute Force
3.1 Selection Sort and Bubble Sort
3.2 Sequential Search and Brute-Force String Matching
3.3 Closest-Pair and Convex-Hull Problems by Brute Force
3.4 Exhaustive Search

Chapter 4. Divide-and-Conquer
4.1 Mergesort
4.2 Quicksort
4.3 Binary Search
4.4 Binary Tree Traversals and Related Properties
4.5 Multiplication of Large Integers and Strassen’s Matrix Multiplication
4.6 Closest-Pair and Convex-Hull Problems by Divide-and-Conquer

Chapter 5. Decrease-and-Conquer
5.1 Insertion Sort
5.2 Depth-First Search and Breadth-First Search
5.3 Topological Sorting
5.4 Algorithms for Generating Combinatorial Objects
5.5 Decrease-by-a-Constant-Factor Algorithms
5.6 Variable-Size-Decrease Algorithms

Chapter 6. Transform-and-Conquer
6.1 Presorting
6.2 Gaussian Elimination
6.3 Balanced Search Trees
6.4 Heaps and Heapsort
6.5 Horner’s Rule and Binary Exponentiation
6.6 Problem Reduction

Chapter 7. Space and Time Tradeoffs
7.1 Sorting by Counting
7.2 Input Enhancement in String Matching
7.3 Hashing
7.4 B-Trees

Chapter 8. Dynamic Programming
8.1 Computing a Binomial Coefficient
8.2 Warshall’s and Floyd’s Algorithms
8.3 Optimal Binary Search Trees
8.4 The Knapsack Problem and Memory Functions

Chapter 9. Greedy Technique
9.1 Prim’s Algorithm
9.2 Kruskal’s Algorithm
9.3 Dijkstra’s Algorithm
9.4 Huffman Trees

Chapter 10. Iterative Improvement
10.1 The Simplex Method
10.2 The Maximum-Flow Problem
10.3 Maximum Matching in Bipartite Graphs
10.4 The Stable Marriage Problem

Chapter 11. Limitations of Algorithm Power
11.1 Lower-Bound Arguments
11.2 Decision Trees
11.3 P, NP, and NP-complete Problems
11.4 Challenges of Numerical Algorithms

Chapter 12. Coping with the Limitations of Algorithm Power
12.1 Backtracking
12.2 Branch-and-Bound
12.3 Approximation Algorithms for NP-hard Problems
12.4 Algorithms for Solving Nonlinear Equations

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