Approximation algorithms for stochastic and risk-averse optimization
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We present improved approximation algorithms in stochastic optimization. We prove that the multi-stage stochastic versions of covering integer programs (such as set cover and vertex cover) admit essentially the same approximation algorithms as their standard (non-stochastic) counterparts; this improves upon work of Swamy & Shmoys which shows an approximability that depends multiplicatively on the number of stages. We also present approximation algorithms for facility location and some of its variants in the 2-stage recourse model, improving on previous approximation guarantees. We give a 2.2975-approximation algorithm in the standard polynomial-scenario model and an algorithm with an expected per-scenario 2.4957-approximation guarantee, which is applicable to the more general black-box distribution model.
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