非凸制約をもつロバスト可解問題に対するランダマイズドアルゴリズム

概要

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A class of robust feasibility problems is considered, which is to find a design variable satisfying a parameter-dependent constraint for all parameter values. A randomized algorithm for solving the problem with a general nonconvex constraint is proposed, where random samples of design variables and uncertain parameters are used. The algorithm stops in a finite number of iterations. Then, it gives a design variable satisfying the constraint for almost all parameter values with a prescribed confidence or says that the problem is infeasible in a probabilistic sense.
公益社団法人計測自動制御学会の論文