Design & Analysis of Algorithms

Design & Analysis of Algorithms

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Lecture -10 Greedy Algorithms -I

10 of 34

10 of 34

Lecture -10 Greedy Algorithms -I

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Design & Analysis of Algorithms

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  1. 1 Lecture - 1 Overview of the course
  2. 2 Lecture - 2 Framework for Algorithms Analysis
  3. 3 Lecture - 3 Algorithms Analysis Framework - II
  4. 4 Lecture - 4 Asymptotic Notation
  5. 5 Lecture -5 Algorithm Design Techniques : Basics
  6. 6 Lecture -6 Divide And Conquer-I
  7. 7 Lecture -7 Divide And Conquer -II Median Finding
  8. 8 Lecture -8 Divide And Conquer -III Surfing Lower Bounds
  9. 9 Lecture -9 Divide And Conquer -IV Closest Pair
  10. 10 Lecture -10 Greedy Algorithms -I
  11. 11 Lecture - 11 Greedy Algorithms - II
  12. 12 Lecture - 12 Greedy Algorithms - III
  13. 13 Lecture - 13 Greedy Algorithms - IV
  14. 14 Lecture - 14 Pattern Matching - I
  15. 15 Lecture - 15 Pattern Matching - II
  16. 16 Lecture -16 Combinational Search and Optimization I
  17. 17 Lecture - 17 Combinational Search and Optimization II
  18. 18 Lecture -18 Dynamic Programming
  19. 19 Lecture 19 Longest Common Subsequences
  20. 20 Lecture -20 Matric Chain Multiplication
  21. 21 Lecture - 21 Scheduling with Startup and Holding Costs
  22. 22 Lecture - 22 Average case Analysis of Quicksort
  23. 23 Lecture - 23 Bipartite Maximum Matching
  24. 24 Lecture - 24 Lower Bounds for Sorting
  25. 25 Lecture -25 Element Distinctness Lower Bounds
  26. 26 Lecture -26 NP-Completeness-I -Motivation
  27. 27 Lecture - 27 NP - Compliteness - II
  28. 28 Lecture - 28 NP-Completeness - III
  29. 29 Lecture - 29 NP-Completeness - IV
  30. 30 Lecture - 30 NP-Completeness - V
  31. 31 Lecture - 31 NP-Completeness - VI
  32. 32 Lecture - 32 Approximation Algorithms
  33. 33 Lecture - 33 Approximation Algorithms
  34. 34 Lecture - 34 Approximation Algorithms for NP

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