Stochastic Optimization using Polynomial Chaos Expansions

TL;DR

This paper introduces a polynomial chaos-based method for efficiently optimizing functions under uncertainty, reducing reliance on costly sampling methods by transforming stochastic problems into deterministic ones.

Contribution

It develops a generalized polynomial chaos approach for stochastic optimization, including error bounds, constraint handling, and demonstration on example problems.

Findings

Orders of magnitude acceleration over Monte Carlo methods for low-dimensional problems

Provides estimates for all moments of the output distribution

Effectively incorporates constraints into the optimization framework

Abstract

Polynomial chaos based methods enable the efficient computation of output variability in the presence of input uncertainty in complex models. Consequently, they have been used extensively for propagating uncertainty through a wide variety of physical systems. These methods have also been employed to build surrogate models for accelerating inverse uncertainty quantification (infer model parameters from data) and construct transport maps. In this work, we explore the use of polynomial chaos based approaches for optimizing functions in the presence of uncertainty. These methods enable the fast propagation of uncertainty through smooth systems. If the dimensionality of the random parameters is low, these methods provide orders of magnitude acceleration over Monte Carlo sampling. We construct a generalized polynomial chaos based methodology for optimizing smooth functions in the presence of random parameters that are drawn from \emph{known} distributions. By expanding the optimization variables using orthogonal polynomials, the stochastic optimization problem reduces to a deterministic one that provides estimates for all moments of the output distribution. Thus, this approach enables one to avoid computationally expensive random sampling based approaches such as Monte Carlo and Quasi-Monte Carlo. In this work, we develop the overall framework, derive error bounds, construct the framework for the inclusion of constraints, analyze various properties of the approach, and demonstrate the proposed technique on illustrative examples.