Combinatorial Optimization Reading Group - Jacob Skitsko

Title: Boosted Sampling

Abstract: We will discuss the boosted sampling technique introduced by Gupta et al. which approximates the stochastic version of problems by using nice approximation algorithms for the deterministic version of the problem. We will focus on rooted stochastic Steiner trees as an example, though other problems are covered by this approach (such as vertex cover and facility location). The problem is given to us in two stages: in the first stage we may choose some elements at a cheaper cost, and in the second stage our actual requirements are revealed to us, and we can buy remaining needed elements at a more expensive cost (where costs get scaled by some factor in the second stage). We will see that if our problem is sub-additive, and we have an alpha-approximation algorithm for the deterministic version of our problem with a beta-strict cost-sharing function then we can get an (alpha + beta)-approximation for the stochastic version of our problem. We also discuss related problems, for example the (not sub-additive!) unrooted stochastic Steiner tree problem.