Algorithms and Theory of Computation Handbook: General Concepts and Techniques

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.

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

Algorithms Design and Analysis Techniques
Searching
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
Computability
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