27th Conference of the International Linear Algebra Society (ILAS 2026)

Reduced Rank Extrapolation for Matrix Equations

May 18, 2026, 4:35 PM

25m

McBryde Hall 113 (Virginia Tech)

Minisymposium Talk Numerical Linear Algebra in Machine Learning Numerical Linear Algebra in Machine Learning

Speaker

Xiaobo Liu (Max Planck Institute for Dynamics of Complex Technical Systems)

Description

Reduced rank extrapolation (RRE) is a classic acceleration method for vector-valued fixed-point process, commonly airsing from iterative solution of algebraic equations. In this talk, we discuss the generalization of this extrapolation framework to sequences of low-rank matrices generated by iterative methods for large-scale matrix equations, such as low-rank alternating directions implicit methods for Lyapunov and Riccati equations, as well as generalized Sylvester equation. We also briefly discuss extrapolation strategies for fixed-point iterations arising in machine learning algorithms.