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Class notes for graduate algorithms class

Sariel Har-Peled
Class notes in one file: here.

(Contains also the chapters missing below.
Slides for the course are here (Fall 2014).

Class notes in separate file per chapter


  1. NP Completeness I
  2. NP Completeness II
  3. NP Completeness III
  4. Dynamic programming
  5. Dynamic programming II - The Recursion Strikes Back
  6. Approximation algorithms
  7. Approximation algorithms II
  8. Approximation algorithms III
  9. Randomized Algorithms
  10. Randomized Algorithms II
  11. Hashing
  12. Min Cut
  13. Network Flow I
  14. Network Flow II
  15. Network Flow III
  16. Network Flow IV
  17. Mincost flow
  18. Mincost flow II
  19. Low dimensional linear programming
  20. Linear programming I
  21. Linear programming II
  22. Linear programming III
  23. FFT
  24. Sorting network
  25. Union find
  26. Max cut
  27. Pereceptron algorithm
  28. Huffman coding
  29. Entropy
  30. Entropy III: Extracting randomness
  31. Shanon theorem
  32. Matchings
  33. Matchings: Maximum weight in bipartite graph, and maximum cardinality in general graph.
  34. Lower bound on sorting and uniqueness.
  35. Backward analysis
  36. Linear time algorithms: Prune and search (linear programmin in 2d)
  37. Streaming

Last modified: Fri 2019-03-08 18:34:26 UTC 2019 by Sariel Har-Peled

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