MIT1_204S10_lec10

# MIT1_204S10_lec10 - 1.204 Lecture 10 Greedy algorithms...

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Unformatted text preview: 1.204 Lecture 10 Greedy algorithms: Knapsack (capital budgeting) Job scheduling Greedy method • Local improvement method – Does not look at problem globally – Takes best immediate step to find a solution – Useful in many cases where • Objectives or constraints are uncertain, or • An approximate answer is all that’s required – Generally O(n) complexity, easy to implement and interpret results • Often requires sorting the data first, which is O(n lg n) – In some cases, greedy algorithms provide optimal solutions (shortest paths, spanning trees, some job scheduling problems) • In most cases they are approximate algorithms • Sometimes used as a part of an exact algorithm (e.g., as a relaxation in an integer programming algorithm) 1 2 General greedy algorithm // Pseudocode public solution greedy(problem) { solution= empty set; problem.sort(); // Usually place elements in order for (element: problem) { if (element feasible and appears optimal) solution= union(solution, element); return solution; } Some greedy algorithms sort, some use a heap, some don’t need to sort at all. Greedy knapsack problem We have n objects, each with weight w i and profit p i . The knapsack has capacity M M w t s x p i n i i ≤ ∑ ∑ < ≤ . . max The knapsack has capacity M. n i w p x M x w i i i i n i i < ≤ ≥ ≥ <= ≤ ≤ ∑ < ≤ , , 1 Greedy knapsack algorithm Algorithm chooses element with highest value/weight ratio first, the next highest second, and so on until it reaches the capacity of the knapsack. This is the same as a gradient or derivative method. Knapsack: integer or not? Let M= 1. Integer solution is {2, 3}, an unexpected result in some contexts. Greedy solution is {1, 98% of 2}. If problem has hard constraints, need integer solution. If constraints are fuzzy, greedy solution may be better. 3 Knapsack problems • Truck packing: integer knapsack – Packing problem in 2 and 3 dimensions is extension Packing problem in 2 and 3 dimensions is extension • Investment program: – Greedy knapsack at high level – Can be integer knapsack at individual transaction level – (Highway investment or telecom capital investment programs often handled as integer problem, with occasionally hard-to- interpret results) – Used to train telecom execs for spectrum auction • Interactions between projects: – Greedy can be extended to handle interactions between small numbers of projects (that can be enumerated) – Integer program handles this explicitly Greedy knapsack code, p.1 public class Knapsack { private static class Item implements Comparable { public double ratio; // Profit/weight ratio public int weight; public Item(double r, int w) { ratio = r; weight = w;...
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## This note was uploaded on 12/04/2011 for the course ESD 1.204 taught by Professor Georgekocur during the Spring '10 term at MIT.

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MIT1_204S10_lec10 - 1.204 Lecture 10 Greedy algorithms...

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