UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2001 Lecture 1 (Part 3) Tuesday, 9/4/01 Greedy Algorithms.

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Presentation transcript:

UMass Lowell Computer Science Analysis of Algorithms Prof. Karen Daniels Fall, 2001 Lecture 1 (Part 3) Tuesday, 9/4/01 Greedy Algorithms

What is a Greedy Algorithm? ä Solves an optimization problem ä Greedy Strategy: ä At each decision point, do what looks best “locally” ä Choice does not depend on evaluating potential future choices or solving subproblems ä Top-down algorithmic structure ä With each step, reduce problem to a smaller problem ä Greedy Choice Property: ä “locally best” = globally best ä Optimal Substructure: ä optimal solution contains in it optimal solutions to subproblems

Examples ä From ä Minimum Spanning Tree ä Dijkstra’s single-source shortest path ä Huffman Codes ä Fractional Knapsack ä Activity Selection

Examples: Activity Selection ä Problem Instance: ä Set S = {1,2,...,n} of n activities ä Each activity i has: ä start time: s i ä finish time : f i ä Activities i, j are compatible iff non-overlapping: ä Objective: ä select a maximum-sized set of mutually compatible activities

Examples: Activity Selection ä Algorithm: ä S’ = presort activities in S by nondecreasing finish time ä and renumber ä GREEDY-ACTIVITY-SELECTOR(S’) ä n length[S’] ä A {1} ä j1 ä for i 2 to n ä do if ä then ä j i ä return A Running time?

Examples: Activity Selection ä Correctness: Board Work

Examples: Activity Selection ä Correctness: Board Work

Another use for Greedy Algorithm ä If optimization problem does not have “greedy choice property”, greedy algorithm may still be useful in bounding the optimal solution ä Example: minimization problem Optimal (unknown value) Upper Bound (heuristic) Lower Bound Solution Values