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Approximation algorithms for stochastic and risk-averse optimization
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 -stage
recourse model, improving on previous approximation guarantees. We give a
-approximation algorithm in the standard polynomial-scenario model and
an algorithm with an expected per-scenario -approximation guarantee,
which is applicable to the more general black-box distribution model.Comment: Extension of a SODA'07 paper. To appear in SIAM J. Discrete Mat
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