Optimal H2 moment matching-based model reduction for linear systems by (non)convex optimization

TL;DR

This paper develops a framework for optimal model reduction of linear systems by matching moments and minimizing the H2-norm error through non-convex and convex optimization techniques, with convergence guarantees.

Contribution

It introduces parametrizations of reduced models, derives optimality conditions, and proposes gradient and SDP relaxation methods for minimal H2-norm error model reduction.

Findings

Gradient methods converge to local optima with guarantees.

Convex SDP relaxations can be exact under certain conditions.

Numerical examples demonstrate the effectiveness of the proposed methods.

Abstract

In this paper we compute families of reduced order models that match a prescribed set of moments of a highly dimensional linear time-invariant system. First, we fully parametrize the models in the interpolation points and in the free parameters, and then we fix the set of interpolation points and parametrize the models only in the free parameters. Based on these two parametrizations and using as objective function the H2-norm of the error approximation we derive non-convex optimization problems, i.e., we search for the optimal free parameters and even the interpolation points to determine the approximation model yielding the minimal H2-norm error. Further, we provide the necessary first-order optimality conditions for these optimization problems given explicitly in terms of the controllability and the observability Gramians of a minimal realization of the error system. Using the optimality conditions, we propose gradient type methods for solving the corresponding optimization problems, with mathematical guarantees on their convergence. We also derive convex SDP relaxations for these problems and analyze when the convex relaxations are exact. We illustrate numerically the efficiency of our results on several test examples.