Unformatted text preview: Conditional Expetations. Let G be a simple graph with vertex set V and edge set E . A cut of a set of vertices V ⊆ V is the number of edges that have one endpoint in V and the other in V \ V . The NPcomplete MaxCut problem asks for the largest cut. A simple randomized approximation problem works as follows: Throw for every vertex a coin. If we got “tails” we add it to V otherwise not. In the end an edge is with probability 1 / 2 in the cut, so the expected value of the cut for V is  E  / 2. Since every cut is at most  E  we have a 2approximation. Use the concept of conditional expectations to derandomize this algorithm. 1...
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 Fall '10
 ErikDemaine
 Data Structures, Probability theory, Conditional expectation, Prof. Erik Demaine, Dr. Andr´ Schulz

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