This greedy approach does not always lead to an

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Unformatted text preview: ly possible. This greedy approach does not always lead to an optimal solution. But there are several problems that it does work optimally for, and such problems are said to possess the greedy-choice property. This is the property that a global optimal configuration can be reached by a series of locally optimal choices (that is, choices that are the best from among the possibilities available at the time), starting from a well-defined configuration. 5.1.1 The Fractional Knapsack Problem Consider the fractional knapsack problem, where we are given a set S of n items, such that each item i has a positive benefit bi and a positive weight wi , and we wish to find the maximum-benefit subset that does not exceed a given weight W . If we are restricted to entirely accepting or rejecting each item, then we would have the 0-1 version of this problem (for which we give a dynamic programming solution in Section 5.3.3). Let us now allow ourselves to take arbitrary fractions of some elements, however. The motivation for this fractional knapsack problem is that we are going on a trip and we have a single knapsack that can carry items that together have weight at most W . In addition, we are allowed to break items into fractions arbitrarily. That is, we can take an amount xi of each item i such that 0 ≤ xi ≤ wi for each i ∈ S and ∑ xi ≤ W. i∈S The total benefit of the items taken is determined by the objective function ∑ bi (xi /wi ). i∈S Consider, for example, a student who is going to an outdoor sporting event and must fill a knapsack full of foodstuffs to take along. Each candidate foodstuff is something that can be easily divided into fractions, such as soda pop, potato chips, popcorn, and pizza. Chapter 5. Fundamental Techniques 260 Algorithm FractionalKnapsack(S, W ): Input: Set S of items, such that each item i ∈ S has a positive benefit bi and a positive weight wi ; positive maximum total weight W Output: Amount xi of each item i ∈ S that maximizes the total benefit while not exceeding the maximum total weight W for each item i ∈ S do xi ← 0 vi ← bi /wi {value index of item i} w←0 {total weight} while w < W do remove from S an item i with highest value index {greedy choice} a ← min{wi , W − w} {more than W − w causes a weight overflow} xi ← a w ← w+a Algorithm 5.1: A greedy algorithm for the fractional knapsack problem. This is one place where greed is good, for we can solve the fractional knapsack problem using the greedy approach shown in Algorithm 5.1. The FractionalKnapsack algorithm can be implemented in O(n log n) time, where n is the number of items in S. Specifically, we use a heap-based priority queue (Section 2.4.3) to store the items of S, where the key of each item is its value index. With this data structure, each greedy choice, which removes the item with greatest value index, takes O(log n) time. To see that the fractional knapsack problem satisfies the greedy-choice property, suppose that there are two items i and j such that xi < wi , x j > 0, and vi < v j . Let y = min{wi − xi , x j }. We could then replace an amount y of item j with an equal amount of item i, thus increasing the total benefit without changing the total weight. Therefore, we can correctly compute optimal amounts for the items by greedily choosing items with the largest value index. This leads to the following theorem. Theorem 5.1: Given a collection S of n items, such that each item i has a benefit bi and weight wi , we can construct a maximum-benefit subset of S, allowing for fractional amounts, that has a total weight W in O(n log n) time. This theorem shows how efficiently we can solve the fractional version of the knapsack problem. The all-or-nothing, or “0-1” version of the knapsack problem does not satisfy the greedy choice property, however, and solving this version of the problem is much harder, as we explore in Sections 5.3.3 and 13.3.4. 5.1. The Greedy Method 261 5.1.2 Task Scheduling Let us consider another optimization problem. Suppose we are given a set T of n tasks, such that each task i has a start time, si , and a finish time, fi (where si < fi ). Task i must start at time si and it is guaranteed to be finished by time fi . Each task has to be performed on a machine and each machine can execute only one task at a time. Two tasks i and j are nonconflicting if fi ≤ s j or f j ≤ si . Two tasks can be scheduled to be executed on the same machine only if they are nonconflicting. The task scheduling problem we consider here is to schedule all the tasks in T on the fewest machines possible in a nonconflicting way. Alternatively, we can think of the tasks as meetings that we must schedule in as few conference rooms as possible. (See Figure 5.2.) Machine 3 Machine 2 Machine 1 1 2 3 4 5 6 7 8 9 An illustration of a solution to the task scheduling problem, for tasks whose collection of pairs of start times and finish times is {(1, 3), (1, 4), (2, 5), (3, 7), (4, 7), (6, 9), (7, 8)}. Figure 5.2: In Algorithm 5.3, we describe a simple greedy algorithm for this problem. Algorithm TaskSchedule(T ): Input: A set T of tasks, such that each task has a start time si and a finish time fi Output: A nonconflicting schedule of the tasks in T using a minimum number of machines m←0 {optimal number of machines} while T = ∅ do remove from T the task i with smallest start time si if there is a mach...
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This document was uploaded on 03/26/2014.

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