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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization
In this paper, we present a Monte-Carlo Compressive Optimization algorithm, a new method for black-box combinatorial optimization, which only assumes that the objective is compressible. The method relies on random queries to the objective function in order to estimate generalized moments. Next, a gr
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