Introduction to the Design Analysis of Algorithms 2nd Edition by Anany Levitin ISBN 0321364139 9780321364135
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Product details:
ISBN 10: 0321364139
ISBN 13: 9780321364135
Author: Anany Levitin
Introduction to the Design Analysis of Algorithms 2nd Table of contents:
Chapter 1: Introduction to Algorithms
1.1 What is an Algorithm?
1.2 Importance of Algorithms in Computer Science
1.3 Algorithm Design and Analysis
1.4 Characteristics of Good Algorithms
1.5 Types of Algorithms: Recursive, Divide and Conquer, Greedy, Dynamic Programming, Backtracking
1.6 Efficiency and Complexity
Chapter 2: Analyzing Algorithms
2.1 Time Complexity and Space Complexity
2.2 Big-O Notation
2.3 Best, Worst, and Average Case Analysis
2.4 Asymptotic Notations: Big-O, Big-Ω, Big-Θ
2.5 Analyzing Recursive Algorithms: Recurrence Relations
2.6 Master Theorem for Divide and Conquer Recurrences
Chapter 3: Divide and Conquer Algorithms
3.1 Introduction to Divide and Conquer
3.2 Merge Sort
3.3 Quick Sort
3.4 Binary Search
3.5 Strassen’s Matrix Multiplication
3.6 General Approach and Applications of Divide and Conquer
Chapter 4: Greedy Algorithms
4.1 Introduction to Greedy Algorithms
4.2 Greedy Choice Property and Optimal Substructure
4.3 Activity Selection Problem
4.4 Huffman Coding
4.5 Knapsack Problem (Fractional and 0/1)
4.6 Prim’s and Kruskal’s Algorithms for Minimum Spanning Tree
4.7 Applications of Greedy Algorithms
Chapter 5: Dynamic Programming
5.1 Introduction to Dynamic Programming (DP)
5.2 Optimal Substructure and Overlapping Subproblems
5.3 Fibonacci Sequence
5.4 Longest Common Subsequence
5.5 Knapsack Problem (Dynamic Programming Solution)
5.6 Matrix Chain Multiplication
5.7 Coin Change Problem
5.8 Applications of Dynamic Programming
Chapter 6: Backtracking Algorithms
6.1 Introduction to Backtracking
6.2 The Backtracking Approach
6.3 N-Queens Problem
6.4 Graph Coloring Problem
6.5 Subset Sum Problem
6.6 Applications of Backtracking
Chapter 7: Network Flow Algorithms
7.1 Introduction to Network Flow Problems
7.2 Maximum Flow Problem
7.3 Ford-Fulkerson Algorithm
7.4 Edmonds-Karp Algorithm
7.5 Minimum-Cost Flow Problem
7.6 Applications of Network Flow Algorithms
Chapter 8: NP-Completeness and Approximation Algorithms
8.1 Introduction to Computational Complexity
8.2 P, NP, and NP-Complete Problems
8.3 Reductions Between Problems
8.4 NP-Complete Problems: Traveling Salesman, Knapsack, etc.
8.5 Approximation Algorithms
8.6 The Importance of Approximation in Practice
Chapter 9: Randomized Algorithms
9.1 Introduction to Randomized Algorithms
9.2 Randomized Quick Sort
9.3 Las Vegas vs. Monte Carlo Algorithms
9.4 Randomized Algorithms in Cryptography
9.5 Applications of Randomized Algorithms
Chapter 10: Computational Geometry
10.1 Introduction to Computational Geometry
10.2 Convex Hull Problem
10.3 Line Segment Intersection
10.4 Voronoi Diagrams
10.5 Geometric Searching Algorithms
Chapter 11: Advanced Data Structures
11.1 Introduction to Advanced Data Structures
11.2 Heaps: Binary Heap, Fibonacci Heap
11.3 AVL Trees and Red-Black Trees
11.4 B-Trees and B+ Trees
11.5 Disjoint Set Data Structures (Union-Find)
11.6 Tries and Suffix Trees
Chapter 12: Parallel Algorithms
12.1 Introduction to Parallel Algorithms
12.2 Parallel Algorithms for Sorting
12.3 Parallel Matrix Multiplication
12.4 Parallel Search Algorithms
12.5 MapReduce and its Applications
Chapter 13: Approximation and Heuristic Algorithms
13.1 Introduction to Approximation Algorithms
13.2 Greedy Approximation Algorithms
13.3 Local Search and Metaheuristics (Simulated Annealing, Genetic Algorithms)
13.4 Performance Analysis of Approximation Algorithms
13.5 Applications of Heuristic Algorithms
Chapter 14: Practical Algorithm Design Techniques
14.1 Problem-Solving Techniques in Practice
14.2 How to Approach Algorithm Design
14.3 Common Algorithmic Strategies
14.4 Debugging and Testing Algorithms
14.5 Optimization and Tuning Algorithms for Performance
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Tags: Anany Levitin, Design Analysis, Algorithms