
Algorithms in a Nutshell
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Content
- Intro
- Algorithms in a Nutshell
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- A Note Regarding Supplemental Files
- Preface
- Principle: Use Real Code, Not Pseudocode
- Principle: Separate the Algorithm from the Problem Being Solved
- Principle: Introduce Just Enough Mathematics
- Principle: Support Mathematical Analysis Empirically
- Audience
- Contents of This Book
- Conventions Used in This Book
- Using Code Examples
- Comments and Questions
- Safari® Books Online
- Acknowledgments
- References
- I. I
- 1. Algorithms Matter
- 1.1. Understand the Problem
- 1.2. Experiment if Necessary
- 1.3. Side Story
- 1.4. The Moral of the Story
- 1.5. References
- 2. The Mathematics of Algorithms
- 2.1. Size of a Problem Instance
- 2.2. Rate of Growth of Functions
- 2.3. Analysis in the Best, Average, and Worst Cases
- 2.4. Performance Families
- 2.5. Mix of Operations
- 2.6. Benchmark Operations
- 2.7. One Final Point
- 2.8. References
- 3. Patterns and Domains
- 3.1. Patterns: A Communication Language
- 3.2. Algorithm Pattern Format
- 3.3. Pseudocode Pattern Format
- 3.4. Design Format
- 3.5. Empirical Evaluation Format
- 3.6. Domains and Algorithms
- 3.7. Floating-Point Computations
- 3.8. Manual Memory Allocation
- 3.9. Choosing a Programming Language
- 3.10. References
- II. II
- 4. Sorting Algorithms
- 4.1. Overview
- 4.2. Insertion Sort
- 4.3. Median Sort
- 4.4. Quicksort
- 4.5. Selection Sort
- 4.6. Heap Sort
- 4.7. Counting Sort
- 4.8. Bucket Sort
- 4.9. Criteria for Choosing a Sorting Algorithm
- 4.10. References
- 5. Searching
- 5.1. Overview
- 5.2. Sequential Search
- 5.3. Binary Search
- 5.4. Hash-based Search
- 5.5. Binary Tree Search
- 6. Graph Algorithms
- 6.1. Overview
- 6.2. Depth-First Search
- 6.3. Breadth-First Search
- 6.4. Single-Source Shortest Path
- 6.5. All Pairs Shortest Path
- 6.6. Minimum Spanning Tree Algorithms
- 6.7. References
- 7. Path Finding in AI
- 7.1. Overview
- 7.2. Depth-First Search
- 7.3. Breadth-First Search
- 7.4. A*Search
- 7.5. Comparison
- 7.6. Minimax
- 7.7. NegMax
- 7.8. AlphaBeta
- 7.9. References
- 8. Network Flow Algorithms
- 8.1. Overview
- 8.2. Maximum Flow
- 8.3. Bipartite Matching
- 8.4. Reflections on Augmenting Paths
- 8.5. Minimum Cost Flow
- 8.6. Transshipment
- 8.7. Transportation
- 8.8. Assignment
- 8.9. Linear Programming
- 8.10. References
- 9. Computational Geometry
- 9.1. Overview
- 9.2. Convex Hull Scan
- 9.3. LineSweep
- 9.4. Nearest Neighbor Queries
- 9.5. Range Queries
- 9.6. References
- III. III
- 10. When All Else Fails
- 10.1. Variations on a Theme
- 10.2. Approximation Algorithms
- 10.3. Offline Algorithms
- 10.4. Parallel Algorithms
- 10.5. Randomized Algorithms
- 10.6. Algorithms That Can Be Wrong, but with Diminishing Probability
- 10.7. References
- 11. Epilogue
- 11.1. Overview
- 11.2. Principle: Know Your Data
- 11.3. Principle: Decompose the Problem into Smaller Problems
- 11.4. Principle: Choose the Right Data Structure
- 11.5. Principle: Add Storage to Increase Performance
- 11.6. Principle: If No Solution Is Evident, Construct a Search
- 11.7. Principle: If No Solution Is Evident, Reduce Your Problem to Another Problem That Has a Solution
- 11.8. Principle: Writing Algorithms Is Hard-Testing Algorithms Is Harder
- IV. IV
- A. Benchmarking
- A.1. Statistical Foundation
- A.2. Hardware
- A.3. Reporting
- A.4. Precision
- About the Authors
- Index
- About the Authors
- Colophon
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