Low rank approximate solutions to large-scale differential matrix Riccati equations

TL;DR

This paper introduces a novel low-rank approximation method for large-scale differential Riccati equations, reducing computational complexity by projecting onto a Krylov subspace before solving.

Contribution

The paper proposes a new dimension reduction approach using Krylov subspace projection for efficiently solving large-scale differential Riccati equations.

Findings

Effective reduction of problem size before integration

Theoretical analysis of residuals and stopping criteria

Numerical experiments demonstrating efficiency

Abstract

In the present paper, we consider large-scale continuous-time differential matrix Riccati equations having low rank right-hand sides. These equations are generally solved by Backward Differentiation Formula (BDF) or Rosenbrock methods leading to a large scale algebraic Riccati equation which has to be solved for each timestep. We propose a new approach, based on the reduction of the problem dimension prior to integration. We project the initial problem onto an extended block Krylov subspace and obtain a low-dimentional differential algebraic Riccati equation. The latter matrix differential problem is then solved by Backward Differentiation Formula (BDF) method and the obtained solution is used to reconstruct an approximate solution of the original problem. We give some theoretical results and a simple expression of the residual allowing the implementation of a stop test in order to limit the dimension of the projection space. Some numerical experiments will be given.