'Optimal substructure property' presentation slideshows

Algorithms analysis and design

Algorithms analysis and design. BY Lecturer: Aisha Dawood. Graph algorithms. MST Prim Kruskal Dijkstra. Minimum Spanning Tree. Problem: given a connected, undirected, weighted graph, find a spanning tree using edges that minimize the total weight. 6. 4. 5. 9. 14. 2. 10. 15. 3.

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CS 2133: Algorithms

CS 2133: Algorithms. Minimum Spanning Tree Shortest Paths. Review: Getting Dressed. Underwear. Socks. Watch. Pants. Shoes. Shirt. Belt. Tie. Jacket. Socks. Underwear. Pants. Shoes. Watch. Shirt. Belt. Tie. Jacket. Review: Topological Sort Algorithm. Topological-Sort() {

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LECTURE 11: Dynamic programming - II -

LECTURE 11: Dynamic programming - II -. Outline. What do we know about dynamic programming ? Application: discrete knapsack Memory functions (memoization) Application: optimal multiplication of matrices Application: transitive closure of a binary relation.

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LECTURE 10: Greedy technique

LECTURE 10: Greedy technique. Outline. Optimization problems Basic idea of greedy technique Examples Correctness verification and efficiency analysis Some classical applications. Optimization problems. The general structure of an optimization problem is: Find x in X such that:

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LECTURE 11: Dynamic programming - I -

LECTURE 11: Dynamic programming - I -. Outline. What is dynamic programming ? Basic steps in applying dynamic programming Bottom-up vs. top-down approaches in developing recurrence relations Applications of dynamic programming. What is dynamic programming ?.

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Greedy Algorithms

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Dynamic Programming

Dynamic Programming. Csc 487/687 Computing for Bioinformatics. Subsequence. A subsequence of a given sequence is just the given sequence with some elements (possible none) left out.

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