From low-rank retractions to dynamical low-rank approximation and back

• Published in: BIT Vol. 64; no. 3; p. 25
• Main Authors: Séguin, Axel, Ceruti, Gianluca, Kressner, Daniel
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2024; Springer Nature B.V
• Subjects: Algorithms; Approximation; Computational Mathematics and Numerical Analysis; Differential equations; Integrators; Manifolds; Mathematics; Mathematics and Statistics; Numeric Computing; Numerical integration; Research Paper; Truncation errors; Dynamical low-rank approximation; 65L05; 65L20; Retraction; Time integration
• ISSN: 0006-3835; 1572-9125; 1572-9125
• DOI: 10.1007/s10543-024-01028-7

In algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More recently, it has been demonstrated that retractions are a useful concept for other computational tasks on manifold as well, including interpolation tasks. In this work, we consider the application of retractions to the numerical integration of differential equations on fixed-rank matrix manifolds. This is closely related to dynamical low-rank approximation (DLRA) techniques. In fact, any retraction leads to a numerical integrator and, vice versa, certain DLRA techniques bear a direct relation with retractions. As an example for the latter, we introduce a new retraction, called KLS retraction, that is derived from the so-called unconventional integrator for DLRA. We also illustrate how retractions can be used to recover known DLRA techniques and to design new ones. In particular, this work introduces two novel numerical integration schemes that apply to differential equations on general manifolds: the accelerated forward Euler (AFE) method and the Projected Ralston–Hermite (PRH) method. Both methods build on retractions by using them as a tool for approximating curves on manifolds. The two methods are proven to have local truncation error of order three. Numerical experiments on classical DLRA examples highlight the advantages and shortcomings of these new methods.