Personalized Optimization for Computer Experiments with Environmental Inputs

TL;DR

This paper introduces a personalized optimization framework for computer experiments with environmental inputs, aiming to find optimal control strategies tailored to varying environmental conditions, outperforming traditional robust methods.

Contribution

The paper develops a novel personalized optimization approach that models optimal control surfaces conditioned on environmental variables, with algorithms for sequential design and approximation.

Findings

Algorithms effectively approximate optimal control surfaces.

Personalized optimization outperforms robust optimization in examples.

Framework applicable to complex computer models.

Abstract

Optimization problems with both control variables and environmental variables arise in many fields. This paper introduces a framework of personalized optimization to han- dle such problems. Unlike traditional robust optimization, personalized optimization devotes to finding a series of optimal control variables for different values of environmental variables. Therefore, the solution from personalized optimization consists of optimal surfaces defined on the domain of the environmental variables. When the environmental variables can be observed or measured, personalized optimization yields more reasonable and better solution- s than robust optimization. The implementation of personalized optimization for complex computer models is discussed. Based on statistical modeling of computer experiments, we provide two algorithms to sequentially design input values for approximating the optimal surfaces. Numerical examples show the effectiveness of our algorithms.