# Minimizing convex quadratic with variable precision conjugate gradients

arXiv: https://arxiv.org/abs/1807.07476 · HTML: https://mlsanity.com/paper/1807.07476/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Low-Precision Arithmetic for Fast Gaussian Processes](https://mlsanity.com/paper/2207.06856/) (2022)
- [VIKING: Deep variational inference with stochastic projections](https://mlsanity.com/paper/2510.23684/) (2025)

## Abstract (arXiv)

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