Design and Analysis of Algorithms

Design and Analysis of Algorithms

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15. Linear Programming: LP, reductions, Simplex

21 of 34

21 of 34

15. Linear Programming: LP, reductions, Simplex

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Classroom Contents

Design and Analysis of Algorithms

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  1. 1 1. Course Overview, Interval Scheduling
  2. 2 2. Divide & Conquer: Convex Hull, Median Finding
  3. 3 R1. Matrix Multiplication and the Master Theorem
  4. 4 3. Divide & Conquer: FFT
  5. 5 R2. 2-3 Trees and B-Trees
  6. 6 4. Divide & Conquer: van Emde Boas Trees
  7. 7 5. Amortization: Amortized Analysis
  8. 8 6. Randomization: Matrix Multiply, Quicksort
  9. 9 R4. Randomized Select and Randomized Quicksort
  10. 10 7. Randomization: Skip Lists
  11. 11 8. Randomization: Universal & Perfect Hashing
  12. 12 R5. Dynamic Programming
  13. 13 9. Augmentation: Range Trees
  14. 14 10. Dynamic Programming: Advanced DP
  15. 15 11. Dynamic Programming: All-Pairs Shortest Paths
  16. 16 12. Greedy Algorithms: Minimum Spanning Tree
  17. 17 R6. Greedy Algorithms
  18. 18 13. Incremental Improvement: Max Flow, Min Cut
  19. 19 14. Incremental Improvement: Matching
  20. 20 R7. Network Flow and Matching
  21. 21 15. Linear Programming: LP, reductions, Simplex
  22. 22 16. Complexity: P, NP, NP-completeness, Reductions
  23. 23 R8. NP-Complete Problems
  24. 24 17. Complexity: Approximation Algorithms
  25. 25 18. Complexity: Fixed-Parameter Algorithms
  26. 26 R9. Approximation Algorithms: Traveling Salesman Problem
  27. 27 19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
  28. 28 20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
  29. 29 R10. Distributed Algorithms
  30. 30 21. Cryptography: Hash Functions
  31. 31 22. Cryptography: Encryption
  32. 32 R11. Cryptography: More Primitives
  33. 33 23. Cache-Oblivious Algorithms: Medians & Matrices
  34. 34 24. Cache-Oblivious Algorithms: Searching & Sorting

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