Algorithms and Theory of Computation Handbook, Volume 1 : General Concepts and Techniques (Chapman & Hall/crc Applied Algorithms and Data Structures series) （2ND）

Atallah, Mikhail J./ Blanton, Marina

CRC Press（2017/05発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 988 p.
• 言語 ENG
• 商品コード 9781138113930
• DDC分類 005.1

Full Description

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.

Contents

Preface, Editors, Contributors, 1 Algorithm Design and Analysis Techniques, 2 Searching, 3 Sorting and Order Statistics, 4 Basic Data Structures, 5 Topics in Data Structures, 6 Multidimensional Data Structures for Spatial Applications, 7 Basic Graph Algorithms, 8 Advanced Combinatorial Algorithms, 9 Dynamic Graph Algorithms, 10 External-Memory Algorithms and Data Structures, 11 Average Case Analysis of Algorithms, 12 Randomized Algorithms, 13 Pattern Matching in Strings, 14 Text Data Compression Algorithms, 15 General Pattern Matching, 16 Computational Number Theory, 17 Algebraic and Numerical Algorithms, 18 Applications of FFT and Structured Matrices, 19 Basic Notions in Computational Complexity, 20 Formal Grammars and Languages, 21 Computability, 22 Complexity Classes, 23 Reducibility and Completeness, 24 Other Complexity Classes and Measures, 25 Parameterized Algorithms, 26 Computational Learning Theory, 27 Algorithmic Coding Theory, 28 Parallel Computation: Models and Complexity Issues, 29 Distributed Computing: A Glimmer of a Theory, 30 Linear Programming, 31 Integer Programming, 32 Convex Optimization, 33 Simulated Annealing Techniques, 34 Approximation Algorithms for NP-Hard Optimization Problems