MIT / Mathematics

Amortized Analysis

By Charles E. Leiserson | Introduction to Algorithms Lecture 13 of 23

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Lecture Description

Course Description

This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.

Course Index

  1. Analysis of Algorithms
  2. Asymptotic Notation and Recurrences
  3. Divide and Conquer
  4. Quicksort
  5. Sorting Lower Bounds and Linear-Time Sorting
  6. Order Statistics
  7. Hashing I
  8. Hashing II
  9. Randomly Built Binary Search Trees
  10. Balanced Search Trees
  11. Augmenting Data Structures
  12. Skip Lists
  13. Amortized Analysis
  14. Competitive Analysis
  15. Dynamic Programming
  16. Greedy Algorithms (and Graphs)
  17. Shortest Paths I
  18. Shortest Paths II
  19. Shortest Paths III
  20. Advanced Topics
  21. Advanced Topics (cont.)
  22. Advanced Topics (cont.)
  23. Advanced Topics (cont.)
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