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13 - Fractional Knapsack

# 13 - Fractional Knapsack - Part IV Greedy Algorithms...

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Part IV: Greedy Algorithms Lecture 13: The Fractional Knapsack Problem Lecture 13: The Fractional Knapsack Problem Part IV: Greedy Algorithms

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Objective and Outline Objective : Illustrate greedy algorithms using the knapsack problem. Reference : Section 16.2 of CLRS Outline Introduction The Knapsack problem. A greedy algorithm for the fractional knapsack problem Correctness Lecture 13: The Fractional Knapsack Problem Part IV: Greedy Algorithms
Introduction to Greedy Algorithm A greedy algorithm for an optimization problem always makes the choice that looks best at the moment and adds it to the current subsolution. Examples already seen Dijkstra’s shortest path algorithm : Select the node, among all “candidate” nodes, that is closest to the source according to estimation d[u]. Prim/Kruskal’s MST algorithms : Select the edge, among all “candidate” edges, that is the lightest. Greedy algorithms don’t always yield optimal solutions but, when they do, they’re usually the simplest and most eﬃcient algorithms available. Lecture 13: The Fractional Knapsack Problem Part IV: Greedy Algorithms

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Outline Introduction The Knapsack problem. A greedy algorithm for the fractional knapsack problem Correctness Lecture 13: The Fractional Knapsack Problem Part IV: Greedy Algorithms
The Knapsack Problem. .. 2pd

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13 - Fractional Knapsack - Part IV Greedy Algorithms...

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