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STOC
2004
ACM

Boosted sampling: approximation algorithms for stochastic optimization

14 years 11 months ago
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
Anupam Gupta, Martin Pál, R. Ravi, Amitabh
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2004
Where STOC
Authors Anupam Gupta, Martin Pál, R. Ravi, Amitabh Sinha
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