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Unformatted text preview: IzaksonasSmith, Evan Problem 1.2 Approximate Cuts Problem The maxcut problem is defined as follows. Given an undirected graph G = ( V,E ), a cut is a set of vertices S ⊂ V . The weight of the cut is the number of edges that cross between S and V \ S , i.e. wt G ( S ) =  E ∩ S × ( V \ S )  . The decision problem maxcut is defined by {h G,k i  G = ( V,E ) ∧∃ S ⊂ V.wt G ( S ) ≥ k } , and the search problem is to find a cut with weight max( G ) = max S ⊂ V wt G ( S ). It is possible to show that maxcut is NPhard. This question will explore randomized algorithms for the search problem. A randomized algorithm is one that can make independent, unbiased coin flips. The output of the algorithm on any given input is thus a random variable, and we can perform the usual operations on random variables (computing functions, taking expectations and other moments, and so on) on this output. (If you feel a little rusty on probability theory, you can review the materials in Appendix A.2 of the textbook)in Appendix A....
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This note was uploaded on 10/21/2011 for the course CSCI 5403 taught by Professor Sturtivant,c during the Spring '08 term at Minnesota.
 Spring '08
 Sturtivant,C

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