2nd Edition

Algorithms and Theory of Computation Handbook, Volume 1 General Concepts and Techniques

By Mikhail J. Atallah, Marina Blanton Copyright 2009
    988 Pages 227 B/W Illustrations
    by Chapman & Hall

    988 Pages 227 B/W Illustrations
    by Chapman & Hall

    Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many of the existing chapters, this second edition contains four new chapters that cover external memory and parameterized algorithms as well as computational number theory and algorithmic coding theory.

    This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics.

    Algorithms Design and Analysis Techniques


    Sorting and Order Statistics

    Basic Data Structures

    Topics in Data Structures

    Multidimensional Data Structures for Spatial Applications

    Basic Graph Algorithms

    Advanced Combinatorial Algorithms

    Dynamic Graph Algorithms

    NEW! External Memory Algorithms and Data Structures

    Average Case Analysis of Algorithms

    Randomized Algorithms

    Pattern Matching in Strings

    Text Data Compression Algorithms

    General Pattern Matching

    NEW! Computational Number Theory

    Algebraic and Numerical Algorithms

    Applications of FFT and Structured Matrices

    Basic Notions in Computational Complexity

    Formal Grammars and Languages


    Complexity Classes

    Reducibility and Completeness

    Other Complexity Classes and Measures

    NEW! Parameterized Algorithms

    Computational Learning Theory

    NEW! Algorithmic Coding Theory

    Parallel Computation: Models and Complexity Issues

    Distributed Computing: A Glimmer of a Theory

    Linear Programming

    Integer Programming

    Convex Optimization

    Simulated Annealing Techniques

    Approximation Algorithms for NP-Hard Optimization Problems


    Mikhail J. Atallah is a distinguished professor of computer science at Purdue University.

    Marina Blanton is an assistant professor in the computer science and engineering department at the University of Notre Dame