Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming
نویسندگان
چکیده
منابع مشابه
Sample Average Approximation with Sparsity- Inducing Penalty for High-Dimensional Stochastic Programming
The theory on the traditional sample average approximation (SAA) scheme for stochastic programming dictates that the number of samples should be polynomial in the number of problem dimensions in order to ensure proper optimization accuracy. In this paper, we study a modification to SAA in the scenario where the global minimizer is either sparse or can be approximated by a sparse solution. By ma...
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ژورنال
عنوان ژورنال: Mathematical Programming
سال: 2018
ISSN: 0025-5610,1436-4646
DOI: 10.1007/s10107-018-1278-0