Quantum mechanics

Computational Science and Engineering

Truncation (statistics)

Algorithm

Truncation error

Linear system

Statistics

Realization (probability)

Applied mathematics

Rank (graph theory)

Model order reduction

Eigenvalues and eigenvectors

Economics

Mathematical optimization

Dentistry

Projection (relational algebra)

Physics

Nonlinear system

Calculus (dental)

Lyapunov equation

Krylov subspace

Mathematics

Lyapunov function

Mathematical analysis

Geometry

Numerical analysis

Combinatorics

Medicine

Finance

Order (exchange)

Reduction (mathematics)

Advances in Computational Mathematics

In this article we investigate model order reduction of large-scale systems using time-limited balanced truncation, which restricts the well known balanced truncation framework to prescribed finite time intervals. The main emphasis is on the efficient numerical realization of this model reduction approach in case of large system dimensions. We discuss numerical methods to deal with the resulting matrix exponential functions and Lyapunov equations which are solved for low-rank approximations. Our main tool for this purpose are rational Krylov subspace methods. We also discuss the eigenvalue decay and numerical rank of the solutions of the Lyapunov equations. These results, and also numerical experiments, will show that depending on the final time horizon, the numerical rank of the Lyapunov solutions in time-limited balanced truncation can be smaller compared to standard balanced truncation. In numerical experiments we test the approaches for computing low-rank factors of the involved Lyapunov solutions and illustrate that time-limited balanced truncation can generate reduced order models having a higher accuracy in the considered time region.

Balanced truncation model order reduction in limited time intervals for large systems