# Faster SVM Training via Conjugate SMO

arXiv: https://arxiv.org/abs/2003.08719 · HTML: https://mlsanity.com/paper/2003.08719/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

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

- [Time-Constrained Learning](https://mlsanity.com/paper/2202.01913/) (2022)
- [An Exact Solver for the Weston-Watkins SVM Subproblem](https://mlsanity.com/paper/2102.05640/) (2021)

## Abstract (arXiv)

We propose an improved version of the SMO algorithm for training classification and regression SVMs, based on a Conjugate Descent procedure. This new approach only involves a modest increase on the computational cost of each iteration but, in turn, usually results in a substantial decrease in the number of iterations required to converge to a given precision. Besides, we prove convergence of the iterates of this new Conjugate SMO as well as a linear rate when the kernel matrix is positive definite. We have implemented Conjugate SMO within the LIBSVM library and show experimentally that it is faster for many hyper-parameter configurations, being often a better option than second order SMO when performing a grid-search for SVM tuning.