The book defines complexity as a numerical function T (n)-the relationship between time and input size n, as one of the basic ideas of computer science. The computational complexity is categorized by algorithm based on its nature and function. The (computational) complexity of the algorithm is a measurement of the ratio of computational resources (time and space) consumed when a particular algorithm is running. For these issues, the book tries to locate heuristic algorithms which can almost explain the problem and operate in a reasonable timeframe. Different kinds of algorithms are described such as graph and network algorithms, algebraic algorithms, parallel algorithms and randomized algorithms.
Author(s): Adele Kuzmiakova
Publisher: Arcler Press
Year: 2020
Language: English
Pages: 254
City: Burlington
Cover
Title Page
Copyright
ABOUT THE EDITOR
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 Basic Techniques for Design and Analysis of Algorithms
1.1. Introduction
1.2. Divide-And-Conquer Algorithms
1.3 Dynamic Programming
1.4. Greedy Heuristics
1.5. Sentinel Linear Search
1.6. Backtracking
1.7. Brute Force/Exhaustive Search
1.8. Branch-And-Bound Algorithm
1.9. Randomized Algorithm
1.10. Branch-H And-Bound
Chapter 2 Computational Complexity Theory
2.1. Introduction
2.2. Brief History
2.3. Computation Models
2.4. Turing Machines
2.5. Computational Problems
2.6. Complexity Classes
2.7. Relationships Between Complexity Classes
2.8. Reducibility And Completeness
2.9. Relativization of P Vs. Np Problem
2.10. Polynomial Hierarchy
2.11. Alternating Complex Classes
2.12. Circuit Complexity
2.13. Probabilistic Complexity Classes
2.14. Interactive Models And Complexity Classes
2.15. Kolmogorov Complexity
Chapter 3 Graph and Network Algorithms
3.1. Introduction
3.2. Tree Traversals
3.3. Depth-First Search
3.4. Algorithm
3.5. Minimum Spanning Trees
Chapter 4 Cryptography
4.1. Introduction
4.2. Quantum Cryptography
4.3. Transmission Media
4.4. History Of Cryptography
4.5. Cryptography Notions Of Security
4.6. Cryptography Building Blocks
4.7. Benefits Of Cryptography
4.8. Drawbacks Of Cryptography
Chapter 5 Algebraic Algorithms
5.1. Introduction
5.2. Computational Methods
5.3. Systems Of Nonlinear Equations And Their Applications
5.4. Polynomial Factorization
Chapter 6 Parallel Algorithms
6.1. Introduction
6.2. Parallelizability
6.3. Dispersed Algorithms
6.4. Parallel Programming Models
6.5. Parallel Algorithm Techniques
6.6. Graphs
6.7. Sorting
6.8. Computational Geometry
6.9. Numerical Algorithms
Chapter 7 Randomized Algorithms
7.1. Introduction
7.2. The Basic Principles Underlying The Construction Of Randomized Algorithms
7.3. Randomized Increasing Constructions In Geometry
7.4. Algorithm Analysis
7.5. De-Randomization
7.6. Example Where Randomness Helps
Chapter 8 Pattern Matching And Text Compression Algorithms
8.1. Introduction
8.2. Processing Texts Efficiently
8.3. Choosing Ml Algorithms
8.4. String-Matching Algorithms
8.5. Two-Dimensional Pattern Matching Algorithms
8.6. Suffix Trees
8.7. Alignment
8.8. Approximate Strinng
8.9. Text Compression
8.10. Research Issues And Summary
8.11. Searching Compressed Dat
Chapter 9 Genetic Algorithms
9.1. Introduction
9.2. Initialization
9.3. Selection
9.4. Genetic Operations
9.5. Heuristics Method
9.6. Termination
9.7. Examples Of Genetic Algorithms
9.8. Underlying Principles In Genetic Algorithm
9.9. Genetic Parameters
9.10. Best Practices In Genetic Algorithm
9.11. Mathematical Analysis Of Genetic Algorithms
9.12. Building Block Hypothesis
9.13. Related Techniques In Genetic Algorithm
9.14. Applications Of Genetic Algorithms
Chapter 10 Combinational Optimization
10.1. Introduction
10.2. Integer Linear Plans
10.3. Algorithms
10.4. Polyhedral Combinatorics
10.5. Incomplete Enumeration Procedures
10.6. Linear Training
10.7. Crucial Substitute of The Algorithms
10.8. Numeral Programming
Bibliography
Index
Back Cover
