This preview shows page 1. Sign up to view the full content.
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 NP-complete 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 2-approximation. Use the concept of conditional expectations to de-randomize this algorithm. 1...
View Full Document
- Fall '10
- Data Structures