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