Preconditioned Krylov solvers for kernel regression

@article{Srinivasan2013PreconditionedKS,
title={Preconditioned Krylov solvers for kernel regression},
author={Balaji Vasan Srinivasan and Qi Hu and Nail A. Gumerov and Raghu Murtugudde and Ramani Duraiswami},
journal={CoRR},
year={2013},
volume={abs/1408.1237}
}

A primary computational problem in kernel regression is solution of a dense linear system with the N × N kernel matrix. Because a direct solution has an O(N) cost, iterative Krylov methods are often used with fast matrix-vector products. For poorly conditioned problems, convergence of the iteration is slow and preconditioning becomes necessary. We investigate preconditioning from the viewpoint of scalability and efficiency. The problems that conventional preconditioners face when applied to… CONTINUE READING