Minimizing convex quadratic with variable precision conjugate gradients
Numerical Analysis
2020-09-22 v3 Numerical Analysis
Optimization and Control
Abstract
We investigate the method of conjugate gradients, exploiting inaccurate matrix-vector products, for the solution of convex quadratic optimization problems. Theoretical performance bounds are derived, and the necessary quantities occurring in the theoretical bounds estimated, leading to a practical algorithm. Numerical experiments suggest that this approach has significant potential, including in the steadily more important context of multi-precision computations
Cite
@article{arxiv.1807.07476,
title = {Minimizing convex quadratic with variable precision conjugate gradients},
author = {S. Gratton and E. Simon and D. Titley-Peloquin and Ph. L. Toint},
journal= {arXiv preprint arXiv:1807.07476},
year = {2020}
}