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2007

LP Rounding Approximation Algorithms for Stochastic Network Design

8 years 2 months ago
LP Rounding Approximation Algorithms for Stochastic Network Design
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field of stochastic optimization deals with such problems where the forecasts are specified in terms of probability distributions of future data. In this paper, we broaden the set of models as well as the techniques being considered for approximating stochastic optimization problems. For example, we look at stochastic models where the cost of the elements is correlated to the set of realized demands, and risk-averse models where upper bounds are placed on the amount spent in each of the stages. These generalized models require new techniques, and our solutions are based on a novel combination of the primal-dual method truncated based on optimal LP relaxation values, followed by a treerounding stage. We use these to give constant-factor approximation algorithms for the stochastic Steiner tree and single sink networ...
Anupam Gupta, R. Ravi, Amitabh Sinha
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where MOR
Authors Anupam Gupta, R. Ravi, Amitabh Sinha
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