Derivative-Free Bound-Constrained Optimization for Solving Structured Problems with Surrogate Models

TL;DR

This paper introduces a model-based derivative-free optimization algorithm tailored for bound-constrained problems with composite smooth objectives, leveraging surrogate models and previous evaluations to improve efficiency in computationally expensive black-box functions.

Contribution

It presents a novel surrogate-model-based DFO algorithm that handles unrelaxable bounds and utilizes prior evaluations for related problems, advancing optimization in black-box settings.

Findings

Outperforms existing DFO algorithms on chemical engineering problems.

Effectively uses surrogate models to reduce evaluation costs.

Handles unrelaxable bound constraints efficiently.

Abstract

We propose and analyze a model-based derivative-free (DFO) algorithm for solving bound-constrained optimization problems where the objective function is the composition of a smooth function and a vector of black-box functions. We assume that the black-box functions are smooth and the evaluation of them is the computational bottleneck of the algorithm. The distinguishing feature of our algorithm is the use of approximate function values at interpolation points which can be obtained by an application-specific surrogate model that is cheap to evaluate. As an example, we consider the situation in which a sequence of related optimization problems is solved and present a regression-based approximation scheme that uses function values that were evaluated when solving prior problem instances. In addition, we propose and analyze a new algorithm for obtaining interpolation points that handles unrelaxable bound constraints. Our numerical results show that our algorithm outperforms a state-of-the-art DFO algorithm for solving a least-squares problem from a chemical engineering application when a history of black-box function evaluations is available.