Mikhail J Atallah, Marina Blanton

Algorithms and Theory of Computation Handbook, Volume 1

General Concepts and Techniques

• Broschiertes Buch

Produktbeschreibung

This handbook 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. 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. Along with updating and revising many of the existing chapters, this edition contains four new chapters that cover external memory and parameterized algorithms as well as computational number theory and algorithmic coding theory.

Produktdetails

• Verlag: CRC Press
• 2nd edition
• Seitenzahl: 988
• Erscheinungstermin: 31. Mai 2017
• Englisch
• Abmessung: 254mm x 178mm x 50mm
• Gewicht: 1678g
• ISBN-13: 9781138113930
• ISBN-10: 113811393X
• Artikelnr.: 50024928

Autorenporträt

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

Inhaltsangabe

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