Authors George Heineman, Gary Pollice and Stanley Selkow have written a very promising book – Algorithms in a Nutshell – published by O’Reilly. The authors are professors at Worcester Polytechnic Institute (WPI). The hardcopy will be available on October 21, 2008, but the book is already available on Safari.
The table of contents :
Part I: I
Chapter 1. Algorithms Matter
Section 1.1. Understand the Problem
Section 1.2. Experiment if Necessary
Section 1.3. Side Story
Section 1.4. The Moral of the Story
Section 1.5. References
Chapter 2. The Mathematics of Algorithms
Section 2.1. Size of a Problem Instance
Section 2.2. Rate of Growth of Functions
Section 2.3. Analysis in the Best, Average, and Worst Cases
Section 2.4. Performance Families
Section 2.5. Mix of Operations
Section 2.6. Benchmark Operations
Section 2.7. One Final Point
Section 2.8. References
Chapter 3. Patterns and Domains
Section 3.1. Patterns: A Communication Language
Section 3.2. Algorithm Pattern Format
Section 3.3. Pseudocode Pattern Format
Section 3.4. Design Format
Section 3.5. Empirical Evaluation Format
Section 3.6. Domains and Algorithms
Section 3.7. Floating-Point Computations
Section 3.8. Manual Memory Allocation
Section 3.9. Choosing a Programming Language
Section 3.10. References
Part II: II
Chapter 4. Sorting Algorithms
Section 4.1. Overview
Section 4.2. Insertion Sort
Section 4.3. Median Sort
Section 4.4. Quicksort
Section 4.5. Selection Sort
Section 4.6. Heap Sort
Section 4.7. Counting Sort
Section 4.8. Bucket Sort
Section 4.9. Criteria for Choosing a Sorting Algorithm
Section 4.10. References
Chapter 5. Searching
Section 5.1. Overview
Section 5.2. Sequential Search
Section 5.3. Binary Search
Section 5.4. Hash-based Search
Section 5.5. Binary Tree Search
Chapter 6. Graph Algorithms
Section 6.1. Overview
Section 6.2. Depth-First Search
Section 6.3. Breadth-First Search
Section 6.4. Single-Source Shortest Path
Section 6.5. All Pairs Shortest Path
Section 6.6. Minimum Spanning Tree Algorithms
Section 6.7. References
Chapter 7. Path Finding in AI
Section 7.1. Overview
Section 7.2. Depth-First Search
Section 7.3. Breadth-First Search
Section 7.4. A*Search
Section 7.5. Comparison
Section 7.6. Minimax
Section 7.7. NegMax
Section 7.8. AlphaBeta
Section 7.9. References
Chapter 8. Network Flow Algorithms
Section 8.1. Overview
Section 8.2. Maximum Flow
Section 8.3. Bipartite Matching
Section 8.4. Reflections on Augmenting Paths
Section 8.5. Minimum Cost Flow
Section 8.6. Transshipment
Section 8.7. Transportation
Section 8.8. Assignment
Section 8.9. Linear Programming
Section 8.10. References
Chapter 9. Computational Geometry
Section 9.1. Overview
Section 9.2. Convex Hull Scan
Section 9.3. LineSweep
Section 9.4. Nearest Neighbor Queries
Section 9.5. Range Queries
Section 9.6. References
Part III: III
Chapter 10. When All Else Fails
Section 10.1. Variations on a Theme
Section 10.2. Approximation Algorithms
Section 10.3. Offline Algorithms
Section 10.4. Parallel Algorithms
Section 10.5. Randomized Algorithms
Section 10.6. Algorithms That Can Be Wrong, but with Diminishing Probability
Section 10.7. References
Chapter 11. Epilogue
Section 11.1. Overview
Section 11.2. Principle: Know Your Data
Section 11.3. Principle: Decompose the Problem into Smaller Problems
Section 11.4. Principle: Choose the Right Data Structure
Section 11.5. Principle: Add Storage to Increase Performance
Section 11.6. Principle: If No Solution Is Evident, Construct a Search
Section 11.7. Principle: If No Solution Is Evident, Reduce Your Problem to Another Problem That Has a Solution
Section 11.8. Principle: Writing Algorithms Is Hard—Testing Algorithms Is Harder
Part IV: IV
Appendix A. Benchmarking
Section A.1. Statistical Foundation
Section A.2. Hardware
Section A.3. Reporting
Section A.4. Precision