Encyclopedia of algorithms 1st Edition by Ming Yang Kao ISBN 9781493928637 by Kao M.-y. (ed.) 9780387301624, 0387301623 instant download after payment.

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ISBN 13: 9781493928637
Author: Ming Yang Kao

This dynamic reference work provides solutions to vital algorithmic problems for scholars, researchers, practitioners, teachers and students in fields such as computer science, mathematics, statistics, biology, economics, financial software, and medical informatics.

This second edition is broadly expanded, building upon the success of its former edition with more than 450 new and updated entries. These entries are designed to ensure algorithms are presented from growing areas of research such as bioinformatics, combinatorial group testing, differential privacy, enumeration algorithms, game theory, massive data algorithms, modern learning theory, social networks, and VLSI CAD algorithms.

Over 630 entries are organized alphabetically by problem, with subentries allowing for distinct solutions. Each entry includes a description of the basic algorithmic problem; the input and output specifications; key results; examples of applications; citations to key literature, open problems, experimental results, links to data sets and downloadable code.

All entries are peer-reviewed, written by leading experts in the field―and each entry contains links to a summary of the author’s research work.

This defining reference is available in both print and online―a dynamic living work with hyperlinks to related entries, cross references citations, and a myriad other valuable URLs.

Encyclopedia of algorithms 1st Table of contents:

I. Fundamental Concepts & General Algorithmic Techniques

• Analysis of Algorithms:
  • Asymptotic Notation (Big O, Omega, Theta)
  • Worst-Case, Average-Case, Amortized Analysis
  • Complexity Classes (P, NP, NP-Complete, NP-Hard)
  • Lower Bounds
• Design Paradigms:
  • Divide and Conquer
  • Dynamic Programming
  • Greedy Algorithms
  • Backtracking and Branch-and-Bound
  • Randomized Algorithms
  • Approximation Algorithms
  • Heuristic Algorithms
• Data Structures:
  • Arrays, Linked Lists, Stacks, Queues
  • Trees (Binary Search Trees, AVL Trees, Red-Black Trees, B-Trees)
  • Heaps (Binary Heaps, Fibonacci Heaps)
  • Hash Tables
  • Graphs (Adjacency List/Matrix)
  • Tries

II. Core Algorithmic Areas

• Graph Algorithms:
  • Graph Traversal (BFS, DFS)
  • Shortest Path Algorithms (Dijkstra, Bellman-Ford, Floyd-Warshall)
  • Minimum Spanning Trees (Prim, Kruskal)
  • Maximum Flow / Minimum Cut
  • Matching Algorithms
  • Topological Sort
  • Connectivity (Strongly Connected Components)
  • Graph Coloring
• Sorting and Searching:
  • Comparison Sorts (Merge Sort, Quick Sort, Heap Sort)
  • Non-Comparison Sorts (Counting Sort, Radix Sort, Bucket Sort)
  • Binary Search
  • Hashing and Hash Functions
• String Algorithms:
  • String Matching (Knuth-Morris-Pratt, Boyer-Moore, Rabin-Karp)
  • Suffix Trees and Suffix Arrays
  • Longest Common Subsequence
  • Edit Distance
• Numerical Algorithms:
  • Matrix Multiplication
  • Fast Fourier Transform (FFT)
  • Number Theory Algorithms (GCD, Primality Testing)
  • Linear Programming
• Computational Geometry:
  • Convex Hull
  • Closest Pair of Points
  • Line Segment Intersection
  • Voronoi Diagrams
• Optimization Algorithms:
  • Simulated Annealing
  • Genetic Algorithms
  • Ant Colony Optimization

III. Specialized & Applied Algorithms

• Cryptography & Security:
  • RSA, AES, Diffie-Hellman
  • Hashing for Cryptography
  • Digital Signatures
• Parallel and Distributed Algorithms:
  • PRAM Model
  • Distributed Consensus
  • MapReduce
• Computational Biology & Bioinformatics:
  • Sequence Alignment (Smith-Waterman, Needleman-Wunsch)
  • Phylogenetic Tree Reconstruction
• Machine Learning Algorithms:
  • Clustering Algorithms (k-Means, Hierarchical)
  • Classification Algorithms (Decision Trees, SVMs)
  • Neural Networks (basic concepts)
  • Regression Algorithms
• Quantum Computing Algorithms:
  • Shor's Algorithm
  • Grover's Algorithm
• Approximation Algorithms:
  • For NP-Hard problems (e.g., Traveling Salesperson, Set Cover)
• Streaming Algorithms:
  • Processing data streams with limited memory

IV. Foundational Theory & Problem Classes

• Computability Theory:
  • Turing Machines
  • Halting Problem
• Complexity Theory:
  • Detailed discussions of P, NP, co-NP, PSPACE, EXPTIME
• Randomized Algorithms:
  • Las Vegas vs. Monte Carlo
  • Probabilistic Analysis
• Online Algorithms:
  • Competitive Analysis

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