Complexity Theory Introduction

May 5th, 2015
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In this appendix, we briefly review the concepts of NP-completeness and reductions. We will use the knapsack problem of Section 3.1 as a running example. Recall that in the knapsack problem, we are given a set of n items I = {1, . . . , n}, where each item i has a value vi and a size si . All sizes and values are positive integers. The knapsack has capacity B, where B is also a positive integer.

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Complexity Theory IntroductionChapter BNP-completenessIn this appendix, we brie?y review the concepts of NP-completeness and reductions. We will use the knapsack problem of Section 3.1 as a running example. Recall that in the knapsack problem, we are given a set of n items I = {1, . . . , n}, where each item i has a value vi and a size si . All sizes and values are positive integers. The knapsack has capacity B, where B is also a positive integer. The goal is to ?nd a subset of items S ? I that maximizes the value ? i?S vi of items in the knapsack subject to the constraint that the total size of these items is ? no more than the capacity; that is, i?S si ? B. Recall the de?nition of a polynomial-time algorithm. De?nition B.1: An algorithm for a problem is said to run in polynomial time, or said to be a polynomial-time algorithm, with respect to a particular model of computer (such as a RAM) if the number of instructions executed by the algorithm can be bounded by a polynomial in the size of the input. More formally, let x denote an instance of a given problem; for example, an instance of the knapsack problem is the number n of items, the numbers si and vi giving the sizes and values of the items, and the number B giving the size of the knapsack. To present the instance as an input to an algorithm A for the problem, we must encode it in bits in some fashion; let |x| be the number of bits in the encoding of x. Then |x| is called the size of the instance or the instance si

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