Low-rank solutions to a class of parametrized systems using Riemannian optimization
This provides a computational framework for large-scale parametrized nonlinear problems, offering efficiency gains for applications in fields like engineering and physics, though it is incremental in extending existing linear results to nonlinear cases.
The paper tackles the problem of computing low-rank approximations for solutions of parametrized systems (linear and nonlinear) by reformulating it as an optimization problem over fixed-rank matrices and using Riemannian optimization techniques, achieving significant computational savings compared to independent solving.
We propose a computational framework for computing low-rank approximations to the ensemble of solutions of a parametrized system of the form $A(ξ)x(ξ)+g(x(ξ))=b(ξ)$ for multiple parameter values. The central idea is to reinterpret the parametrized system as the first-order optimality condition of an optimization problem set over the space of real matrices, which is then minimized over the manifold of fixed-rank matrices. This formulation enables the use of Riemannian optimization techniques, including conjugate gradient and trust-region methods, and covers both linear and nonlinear instances under mild assumptions on the structure of the parametrized system. We further provide a theoretical analysis establishing conditions under which the solution matrix admits accurate low-rank approximations, extending existing results from linear to nonlinear problems. To enhance computational efficiency and robustness, we discuss tailored preconditioning strategies and a rank-compression mechanism to control the rank growth induced by nonlinearities. Numerical experiments demonstrate that the proposed approach achieves significant computational savings compared to solving each system independently, as well as highlight the potential of Riemannian optimization methods for low-rank approximations in large-scale parametrized nonlinear problems.