RCGLS replaces the gradient in CGLS with a randomized coordinate version via a constraint correction view, proving linear convergence in expectation better than randomized coordinate descent, plus sparse implementation and ridge regression extension.
Linear convergence of Gearhart-Koshy accelerated Kaczmarz methods for tensor linear systems
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abstract
The generalized Gearhart-Koshy acceleration is a recent exact affine search technique designed for the method of cyclic projections onto hyperplanes, i.e., the Kaczmarz method. However, its convergence properties, particularly the linear convergence rate, have not been thoroughly established. In this paper, we systematically establish the linear convergence of the generalized Gearhart-Koshy accelerated Kaczmarz method for tensor linear systems, proving that it converges linearly to the unique least-norm solution. Our analysis is general and applies to several popular Kaczmarz variants, including incremental, shuffle-once, and random-reshuffling schemes, and demonstrates that this acceleration approach yields a better convergence upper bound compared to the plain Kaczmarz method. We also propose an efficient Gram-Schmidt-based implementation that computes the next iterate in linear time. Building on this implementation, we establish a connection between this acceleration framework and Arnoldi-type Krylov subspace methods, further highlighting its efficiency and potential. Our theoretical results are supported by numerical experiments.
fields
math.NA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.
citing papers explorer
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Randomized conjugate gradient least squares
RCGLS replaces the gradient in CGLS with a randomized coordinate version via a constraint correction view, proving linear convergence in expectation better than randomized coordinate descent, plus sparse implementation and ridge regression extension.
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On subspace-constrained preconditioning for randomized iterative methods
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.