Boosted sampling: approximation algorithms for stochastic optimization

Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER TREE problem, for example, edges must be chosen to connect terminals (clients); in VERTEX COVER, vertices must be chosen to cover edges (clients); in FACILITY LOCATION, facilities must be chosen and demand vertices (clients) connected to these chosen facilities. We consider a stochastic version of such a problem where the solution is constructed in two stages: Before the actual requirements materialize, we can choose elements in a first stage. The actual requirements are then revealed, drawn from a pre-specified probability distribution ; thereupon, some more elements may be chosen to obtain a feasible solution for the actual requirements. However, in this second (recourse) stage, choosing an element is costlier by

• Type: Conference
• Year: 2004
• Where: STOC
• Authors: Anupam Gupta, Martin Pál, R. Ravi, Amitabh Sinha
• Keywords: Actual Requirements, Algorithms, Feasible Solution, Problems Choose Elements, STOC 2004