Validation analysis of mirror descent stochastic approximation method |
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Authors: | Guanghui Lan Arkadi Nemirovski Alexander Shapiro |
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Institution: | 1. University of Florida, Gainesville, FL, 32611, USA 2. Georgia Institute of Technology, Atlanta, GA, 30332, USA
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Abstract: | The main goal of this paper is to develop accuracy estimates for stochastic programming problems by employing stochastic approximation (SA) type algorithms. To this end we show that while running a Mirror Descent Stochastic Approximation procedure one can compute, with a small additional effort, lower and upper statistical bounds for the optimal objective value. We demonstrate that for a certain class of convex stochastic programs these bounds are comparable in quality with similar bounds computed by the sample average approximation method, while their computational cost is considerably smaller. |
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