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Approximation Algorithms for Reliable Stochastic Combinatorial Optimization
[chapter]

2010
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Lecture Notes in Computer Science
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We consider optimization problems that can be formulated as minimizing the cost of a feasible solution w T x over an arbitrary combinatorial feasible set F ⊂ {0, 1} n . For these problems we describe a broad class of corresponding stochastic problems where the cost vector W has independent random components, unknown at the time of solution. A natural and important objective that incorporates risk in this stochastic setting is to look for a feasible solution whose stochastic cost has a small

doi:10.1007/978-3-642-15369-3_26
fatcat:odnjeoieenad7eahwd45abb3ru