ロバスト最適化のための逐次的ランダマイズドアルゴリズム

Abstract

A probabilistic approach is considered for robust optimization, where a convex objective function is minimized subject to a parameter dependent convex constraint. A novel sequential randomized algorithm is proposed for solving this optimization employing the stochastic ellipsoid method. It is shown that the upper bounds of the numbers of random samples and updates of the algorithm are much less than those of the stochastic bisection method utilizing the stochastic ellipsoid method at each iteration. This feature actually leads to a computational advantage, which is demonstrated through a numerical example.