Algorithms Illuminated teaches the basics of algorithms in the most accessible way imaginable, with thorough coverage of asymptotic analysis, graph search and shortest paths, data structures, divide-and-conquer algorithms, greedy algorithms, dynamic programming, and NP-hard problems.
Algorithms Illuminated teaches the basics of algorithms in the most accessible way imaginable, with thorough coverage of asymptotic analysis, graph search and shortest paths, data structures, divide-and-conquer algorithms, greedy algorithms, dynamic programming, and NP-hard problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Tim Roughgarden is a Professor of Computer Science at Columbia University. His research, teaching, and expository writings have been recognized by a Presidential Early Career Award for Scientists and Engineers, the ACM Grace Murray Hopper Award, the EATCS-SIGACT Gödel Prize, a Guggenheim Fellowship, the INFORMS Lancaster Prize, and a Tau Beta Pi Teaching Award. His other books include Twenty Lectures on Algorithmic Game Theory (2016) and Beyond the Worst-Case Analysis of Algorithms (2021).
Inhaltsangabe
Part I. The Basics: 1. Introduction 2. Asymptotic notation 3. Divide-and-Conquer algorithms 4. The master method 5. QuickSort 6. Linear-time selection Part II. Graph Algorithms and Data Structures: 7. Graphs: the Basics 8. Graph search and its applications 9. Dijkstra's shortest-path algorithm 10. The heap data structure 11. Search trees 12. Hash tables and Bloom filters Part III. Greedy Algorithms and Dynamic Programming 13. Introduction to greedy algorithms 14. Huffman codes 15. Minimum spanning trees 16. Introduction to dynamic programming 17. Advanced dynamic programming 18. Shortest paths revisited Part IV. Algorithms for NP-Hard Problems 19. What is NP-Hardness? 20. Compromising on correctness: efficient inexact algorithms 21. Compromising on speed: exact inefficient algorithms 22. Proving problems NP-hard 23. P, NP, and all that 24. Case study: the FCC incentive auction Appendix A. Quick review of proofs By induction Appendix B. Quick review of discrete probability Epilogue. A field guide to algorithm design Hints and solutions.
Part I. The Basics: 1. Introduction 2. Asymptotic notation 3. Divide-and-Conquer algorithms 4. The master method 5. QuickSort 6. Linear-time selection Part II. Graph Algorithms and Data Structures: 7. Graphs: the Basics 8. Graph search and its applications 9. Dijkstra's shortest-path algorithm 10. The heap data structure 11. Search trees 12. Hash tables and Bloom filters Part III. Greedy Algorithms and Dynamic Programming 13. Introduction to greedy algorithms 14. Huffman codes 15. Minimum spanning trees 16. Introduction to dynamic programming 17. Advanced dynamic programming 18. Shortest paths revisited Part IV. Algorithms for NP-Hard Problems 19. What is NP-Hardness? 20. Compromising on correctness: efficient inexact algorithms 21. Compromising on speed: exact inefficient algorithms 22. Proving problems NP-hard 23. P, NP, and all that 24. Case study: the FCC incentive auction Appendix A. Quick review of proofs By induction Appendix B. Quick review of discrete probability Epilogue. A field guide to algorithm design Hints and solutions.
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