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Typical properties of winners and losers in discrete optimization
2004
Proceedings of the thirty-sixth annual ACM symposium on Theory of computing - STOC '04
We present a probabilistic analysis for a large class of combinatorial optimization problems containing, e.g., all binary optimization problems defined by linear constraints and a linear objective function over {0, 1} n . By parameterizing which constraints are of stochastic and which are of adversarial nature, we obtain a semirandom input model that enables us to do a general average-case analysis for a large class of optimization problems while at the same time taking care for the
doi:10.1145/1007352.1007409
dblp:conf/stoc/BeierV04
fatcat:2bwg7ny4yfcu3kzt3sne3p5f5u