Dynamic Sampling for Feasibility Determination

Abstract

We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.