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A Short Survey of Aproximation Algorithms for Combinatorial Optimization under Uncertainty∗

This paper briefly describes three well-established frameworks for handling uncertainty in optimization problems. Our focus is mainly on combinatorial optimization and on the development of approximation algorithms under the discussed frameworks. In particular, we give a brief overview of Stochastic Programming, Robust Optimization, and Probabilistic Combinatorial Optimization, and list approximation results from the wealth of recent literature on combinatorial problems under these disciplines.