ASTROMoRF: Adaptive Sampling Trust-Region Optimization with Dimensionality Reduction

High dimensional simulation optimization problems have become prevalent in recent years. In practice, the objective function is typically influenced by a lower dimensional combination of the original decision variables, and implementing dimensionality reduction can improve the efficiency of the optimization algorithm. In this paper, we introduce a novel algorithm ASTROMoRF that combines adaptive sampling with dimensionality reduction, using an iterative trust-region approach. Within a trust-region algorithm a series of surrogates or metamodels is built to estimate the objective function. Using a lower dimensional subspace reduces the number of design points needed for building a surrogate within each trust-region and consequently the number of simulation replications. We explain the basis for the algorithm within the paper and compare its finite-time performance with other state-of-the-art solvers.