Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints

Abstract

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In many real-world applications of optimization, the underlying descriptive system model is defined by computationally expensive functions: simulation modules, numerical models and other “black box” model components are typical examples. In such cases, the model development and optimization team often has to rely on optimization carried out under severe resource constraints. To address this important issue, recently a Regularly Spaced Sampling (RSS) module has been added to the Lipschitz Global Optimizer (LGO) solver suite. RSS generates non-collapsing space filling designs, and produces corresponding solution estimates: this information is passed along to LGO for refinement within the given resource (function evaluation and/or runtime) limitations. Obviously, the quality of the solution obtained will essentially depend both on model instance difficulty and on the admissible computational effort. In spite of this general caveat, our results based on solving a selection of non-trivial global optimization test problems suggest that even a moderate amount of well-placed sampling effort enhanced by limited optimization can lead at least to reasonable or even to high quality results. Our numerical tests also indicate that LGO’s overall efficiency is often increased by using RSS as a presolver, both in resource-constrained and in completed LGO runs.Hungarian National Development Agency ; European Unio

Benchmarking the LGO Solver Suite within the COCO Test Environment

“…This is followed first by a regularly spaced sampling (RSS) presolver mode and another quick global presolver: each of these searches is followed by a LS phase. The RSS solver component is described in [24]. If further search is implied by opmode and it is supported by LGO's calling parameters and its internal resource allocation strategy, then one of BB + LS, GARS + LS, and MS + a set of LS phases follow.…”