1. Thinking in Algorithms Understand the Problem Naive Solution Intelligent Approaches Summary References
2. The Mathematics of Algorithms Size of a Problem Instance Rate of Growth of Functions Analysis in the Best, Average, and Worst Cases Performance Families Benchmark Operations References
3. Algorithm Building Blocks Algorithm Template Format Pseudocode Template Format Empirical Evaluation Format Floating-Point Computation Example Algorithm Common Approaches References
4. Sorting Algorithms Transposition Sorting Selection Sort Heap Sort Partition-Based Sorting Sorting without Comparisons Bucket Sort Sorting with Extra Storage String Benchmark Results Analysis Techniques References
6. Graph Algorithms Graphs Depth-First Search Breadth-First Search Single-Source Shortest Path Dijkstra's Algorithm for Dense Graphs Comparing Single-Source Shortest-Path Options All-Pairs Shortest Path Minimum Spanning Tree Algorithms Final Thoughts on Graphs References
7. Path Finding in AI Game Trees Path-Finding Concepts Minimax NegMax AlphaBeta Search Trees Depth-First Search Breadth-First Search A'Search Comparing Search-Tree Algorithms References
8. Network Flow Algorithms Network Flow Maximum Flow Bipartite Matching Reflections on Augmenting Paths Minimum Cost Flow Transshipment Transportation Assignment Linear Programming References
10. Spatial Tree Structures Nearest Neighbor Queries Range Queries Intersection Queries Spatial Tree Structures Nearest Neighbor Queries Range Query Quadtrees R-Trees References
11. Emerging Algorithm Categories Variations on a Theme Approximation Algorithms Parallel Algorithms Probabilistic Algorithms References
12. Epilogue: Principles of Algorithms Know Your Data Decompose a Problem into Smaller Problems Choose the Right Data Structure Make the Space versus Time Trade-Off Construct a Search Reduce Your Problem to Another Problem Writing Algorithms Is Hard-Testing Algorithms Is Harder Accept Approximate Solutions When Possible Add Parallelism to Increase Performance A. Benchmarking
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