Analysis and Design of Algorithms 1st edition by Amrinder Arora ISBN 1634870212 9781634870214

Analysis and Design of Algorithms 1st edition by Amrinder Arora – Ebook PDF Instant Download/Delivery. 1634870212, 978-1634870214
Full download Analysis and Design of Algorithms 1st Edition after payment

 

Product details:

ISBN 10:       1634870212
ISBN 13: 978-1634870214
Author:         Amrinder Arora

The book begins with a clear explanation of the basics: what algorithms are, their practical applications, asymptotic notation, and data structures. The second section covers the algorithmic design techniques of divide and conquer, greedy, dynamic programming, branch and bound, and graph traversal. For each of these techniques, the book presents templates and guidelines on when to use and not to use each technique. The third major section of the book covers NP-completeness and the inherent hardness of problems.

Using the material provided in this book, students and professionals can master the processes to use in solving the most difficult algorithmic problems. Users can explore various techniques, and learn to decide which algorithm design technique to use for a given problem. Many sections contain innovative mnemonics to aid the readers in remembering the templates and key takeaways.

Analysis and Design of Algorithms 1st Table of contents:

Part 1: Introduction and Fundamentals

1. Introduction to Algorithms

   • What is an Algorithm?
   • Role of Algorithms in Computer Science
   • Algorithmic Problem Solving
   • Analyzing Algorithms: Time and Space Complexity

2. Mathematical Foundations

   • Mathematical Preliminaries
   • Big-O Notation and Growth of Functions
   • Recurrences and Their Solution Methods
   • Solving Recurrence Relations

Part 2: Divide and Conquer Algorithms

3. Divide and Conquer: Overview

   • Concept of Divide and Conquer
   • Key Properties of Divide and Conquer Algorithms
   • General Methodology for Solving Problems

4. Merge Sort

   • Introduction to Sorting Algorithms
   • Merge Sort Algorithm
   • Time Complexity Analysis of Merge Sort
   • Variations and Optimizations

5. Quick Sort

   • Quick Sort Algorithm
   • Partitioning and Recursion in Quick Sort
   • Average and Worst Case Time Complexity
   • Optimizations in Quick Sort

6. Strassen’s Matrix Multiplication

   • Matrix Multiplication Overview
   • Strassen’s Algorithm for Faster Matrix Multiplication
   • Time Complexity Analysis and Optimization

Part 3: Dynamic Programming

7. Introduction to Dynamic Programming

   • Key Concepts of Dynamic Programming
   • Overlapping Subproblems and Optimal Substructure
   • Memoization vs. Tabulation Approach

8. Fibonacci Numbers

   • Naive Approach vs. Dynamic Programming Approach
   • Time Complexity and Space Complexity

9. Shortest Path Problems

   • Bellman-Ford Algorithm
   • Dijkstra’s Algorithm
   • Floyd-Warshall Algorithm
   • Applications in Graphs and Networks

10. Longest Common Subsequence (LCS)

    • Dynamic Programming for LCS
    • Matrix Representation of LCS
    • Time Complexity and Applications

11. Knapsack Problem

    • 0/1 Knapsack Problem
    • Fractional Knapsack
    • Greedy vs. Dynamic Programming Approach

Part 4: Greedy Algorithms

12. Introduction to Greedy Algorithms

    • What Makes an Algorithm Greedy?
    • Greedy Methodology and Problem Solving
    • Characteristics of Greedy Algorithms

13. Activity Selection Problem

    • Interval Scheduling and Greedy Approach
    • Time Complexity Analysis

14. Huffman Coding

    • Data Compression with Huffman Coding
    • Greedy Approach for Encoding
    • Optimality and Time Complexity

Part 5: Graph Algorithms

15. Graph Representations and Traversals

    • Adjacency Matrix vs. Adjacency List
    • Depth-First Search (DFS)
    • Breadth-First Search (BFS)

16. Minimum Spanning Tree

    • Kruskal’s Algorithm
    • Prim’s Algorithm
    • Time Complexity and Applications

17. Shortest Path in Graphs

    • Bellman-Ford Algorithm
    • Dijkstra’s Algorithm
    • A* Algorithm and Applications

18. Topological Sorting

    • Directed Acyclic Graphs (DAG)
    • Topological Sorting Algorithms
    • Applications in Scheduling

Part 6: Advanced Algorithms

19. Network Flow Algorithms

    • Max Flow Min Cut Theorem
    • Ford-Fulkerson Algorithm
    • Edmonds-Karp Algorithm

20. NP-Completeness

    • Introduction to NP-Completeness
    • P vs NP Problem
    • NP-Hard and NP-Complete Problems
    • Cook’s Theorem and Reductions

21. Approximation Algorithms

    • Approximation Techniques for NP-Hard Problems
    • Greedy Approximations
    • Approximation Ratios and Performance Guarantees

Part 7: Parallel and Distributed Algorithms

22. Parallel Algorithms

    • Introduction to Parallelism
    • Parallel Algorithms for Sorting and Searching
    • Time Complexity in Parallel Algorithms

23. Distributed Algorithms

    • Distributed Computing Concepts
    • Distributed Consensus Algorithms
    • Challenges in Designing Distributed Algorithms

Part 8: Miscellaneous Topics

24. Randomized Algorithms

    • Introduction to Randomized Algorithms
    • Las Vegas and Monte Carlo Algorithms
    • Applications and Efficiency of Randomized Algorithms

25. String Matching Algorithms

    • Naive String Matching
    • Knuth-Morris-Pratt (KMP) Algorithm
    • Boyer-Moore Algorithm
    • String Matching in Real-Life Applications

People also search for Analysis and Design of Algorithms 1st:

binary search in analysis and design of algorithms
introduction to analysis and design of algorithms
best book for analysis and design of algorithms
quick sort in analysis and design of algorithms
analysis and design of algorithms gtu syllabus