# An ADMM Solver for the MKL-L0/1-SVM

arXiv: https://arxiv.org/abs/2303.04445 · HTML: https://mlsanity.com/paper/2303.04445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [On the Global Linear Convergence of the ADMM with Multi-Block Variables](https://mlsanity.com/paper/1408.4266/) (2014)

## Built on by (most cited first, 1 of 1)

- [MKL-L0/1-SVM](https://mlsanity.com/paper/2308.12016/) (2023)

## Abstract (arXiv)

We formulate the Multiple Kernel Learning (abbreviated as MKL) problem for the support vector machine with the infamous (0,1)-loss function. Some first-order optimality conditions are given and then exploited to develop a fast ADMM solver for the nonconvex and nonsmooth optimization problem. A simple numerical experiment on synthetic planar data shows that our MKL-L0/1-SVM framework could be promising.