Lecture Topics W 2012

Note: lectures with 12 dates have been updated, others are left over from last year and may not be accurate

  • 1. 1/9/12. Introduction (Types of analysis, motivation, overview)
  • Dynamic Programming: Points to lines (6.3), Properties of Dynamic programming (6.2),

  • 2. 1/11/12. RNA folding (6.5) into to Sequence alignment (6.6)

  • 3. 1/13/12 sequence alignment (6.6), linear space (6.7), intro to shortest paths (6.8)
  • 4. 1/18/12 More on shortest paths: (6.8,6.9, bit of 6.10), All pairs shortest paths: Floyd-Warshall;

  • 5. 1/20/12 Network Flow intro (7), Residual graphs, 7.2 Ford-Fulkerson algorithm,

  • 6. 1/23/12 More Ford-Fulkerson (analysis and Max-Flow Min-cut Thm) 7.2, 7.3 capacity scaling algorithm,

  • 7. 1/25/12 Improved Scaling algorithm (using d() values to efficiently find shortest A-paths), Notes on an efficient algorithm for th e shortest A-path

  • 8. 1/27/12 Analysis of efficient Shortest A-path algorithm. Applications: bipartite matching (7.5),

  • 9. 1/30/12 disjoint paths, global min-cut (handout on web page)

  • 10. 2/1/12 Project Selection (7.11), extensions to network flow (7.7), survey design (7.8)

  • 11. 2/3/12 weighted bipartite matching (7.13), non-bipartite matching

  • 12. 2/6/12 Min-cost flows, cheapest disjoint paths application; linear programming (11.6)

  • 13. 2/8/12 Hard Problems: P, reductions, hard problems (8.1, 8.2) Hard Problems: (8.2),

  • 14. 2/10/12 MIDTERM

  • 15. 2/13/12 Midterm comments. NP (8.3), NP-complete (8.4), NP decision versus optimization,

  • 16. 2/15/12 What does NP-hard really mean? Dealing with NP-hard problems. Pspace (9.1,9.2) Dealing with hard problems, special cases: 10.1 Small Vertex Covers,

  • 17. 2/17/12 Small vertex covers, 10.2 Independent Set on trees

  • 18. 2/22/12 Approximations, Scheduling 11.1, Vertex Cover,

  • 19. 2/24/12 center selection 11.2, Set cover 11.3,

  • 20. 2/27/12 set cover continued, weighted vertex cover 11.4

  • 21. 2/29/12 vertex cover: 11.4 and linear programming/integer programming pprox 11.6, TSP is hard to approximate

  • 22. 3/2/12 11.8 knapsack, problems that are easy/hard to approximate.

  • 23. 3/5/12 Local Search (12.1), Simulated Annealing (brief) (12.2) Randomized algorithms: contention resolution (13.1),

  • 24. 3/7/12 contention resolution continued, randomized Min-cut (13.2)

  • 25. 3/9/12 randomized max-sat (13.4), Hashing (13.6)

  • 26. 3/12/12 Perfect Hashing (CLRS 11.5) Primality Testing (see link, and Cormen, Leiserson, Rivest 31.8),

  • 27. 3/14/12 Primality Testing (see link, and Cormen, Leiserson, Rivest 31.8),

  • 28. 3/16/12 (taped on 3/14/12) Closest Point (13.7),

  • 29. 3/19/12 Baseball Elimination (Which teams can still win?) (7.12) PRAM models CRCS, CREW, EREW, parallel summation/max/OR. Parallel Quicksort Parallel quicksort using pointer , bitonic sort (27.3 in Cormebook) K