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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Introduction to Algorithms (Fall 2005) (M-I-T) » Lecture 15: Dynamic Programming, Longest Common Subsequence (M-I-T)

Lecture 15: Dynamic Programming, Longest Common Subsequence (M-I-T)

Course:Introduction to Algorithms (Fall 2005) (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Charles Leiserson, Prof. Erik Demaine
Level:Undergraduate

Introduction to Algorithms (Fall 2005) (M-I-T)

Lecture 10: Red-black Trees, Rotations, Insertions, Deletions (M-I-T)
Lecture 11: Augmenting Data Structures, Dynamic Order Statistics, Interval Trees (M-I-T)
Lecture 12: Skip Lists (M-I-T)
Lecture 13: Amortized Algorithms, Table Doubling, Potential Method (M-I-T)
Lecture 14: Competitive Analysis: Self-organizing Lists (M-I-T)
Lecture 15: Dynamic Programming, Longest Common Subsequence (M-I-T)
Lecture 16: Greedy Algorithms, Minimum Spanning Trees (M-I-T)
Lecture 17: Shortest Paths I: Properties, Dijkstra's Algorithm, Breadth-first Search (M-I-T)
Lecture 18: Shortest Paths II: Bellman-Ford, Linear Programming, Difference Constraints (M-I-T)
Lecture 19: Shortest Paths III: All-pairs Shortest Paths, Matrix Multiplication, Floyd-Warshall, Johnson (M-I-T)
Lecture 1: Administrivia; Introduction; Analysis of Algorithms, Insertion Sort, Mergesort (M-I-T)
Lecture 22: Advanced Topics (M-I-T)
Lecture 23: Advanced Topics (cont.) (M-I-T)
Lecture 24: Advanced Topics (cont.) (M-I-T)
Lecture 25: Advanced Topics (cont.) – Discussion of Follow-on Classes (M-I-T)
Lecture 2: Asymptotic Notation; Recurrences; Substitution, Master Method (M-I-T)
Lecture 3: Divide-and-Conquer: Strassen, Fibonacci, Polynomial Multiplication (M-I-T)
Lecture 4: Quicksort, Randomized Algorithms (M-I-T)
Lecture 5: Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort (M-I-T)
Lecture 6: Order Statistics, Median (M-I-T)

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