Rank-$1$ matrix differential equations for structured eigenvalue optimization

TL;DR

This paper introduces a novel rank-1 matrix differential equation approach for structured eigenvalue optimization, significantly reducing computational costs by exploiting the manifold of rank-1 matrices.

Contribution

It develops a new method using rank-1 matrix manifolds and gradient projection for efficient eigenvalue optimization of large structured matrices.

Findings

The method effectively computes structured pseudospectra extremal points.

It reduces storage and computational costs for large matrices.

Near a local minimum, the projected gradient system closely approximates the full gradient system.

Abstract

A new approach to solving eigenvalue optimization problems for large structured matrices is proposed and studied. The class of optimization problems considered is related to computing structured pseudospectra and their extremal points, and to structured matrix nearness problems such as computing the structured distance to instability or to singularity. The structure can be a general linear structure and includes, for example, large matrices with a given sparsity pattern, matrices with given range and co-range, and Hamiltonian matrices. Remarkably, the eigenvalue optimization can be performed on the manifold of complex (or real) rank-1 matrices, which yields a significant reduction of storage and in some cases of the computational cost. The method relies on a constrained gradient system and the projection of the gradient onto the tangent space of the manifold of complex rank-$1$ matrices. It is shown that near a local minimizer this projection is very close to the identity map, and so the computationally favorable rank-1 projected system behaves locally like the %computationally expensive gradient system.