# A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification

arXiv: https://arxiv.org/abs/1502.01563 · HTML: https://mlsanity.com/paper/1502.01563/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2015.

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

- [Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization](https://mlsanity.com/paper/2203.12633/) (2022)
- [Faster Rates for the Frank-Wolfe Algorithm Using Jacobi Polynomials](https://mlsanity.com/paper/2110.09738/) (2021)

## Abstract (arXiv)

Frank-Wolfe algorithms have recently regained the attention of the Machine Learning community. Their solid theoretical properties and sparsity guarantees make them a suitable choice for a wide range of problems in this field. In addition, several variants of the basic procedure exist that improve its theoretical properties and practical performance. In this paper, we investigate the application of some of these techniques to Machine Learning, focusing in particular on a Parallel Tangent (PARTAN) variant of the FW algorithm that has not been previously suggested or studied for this type of problems. We provide experiments both in a standard setting and using a stochastic speed-up technique, showing that the considered algorithms obtain promising results on several medium and large-scale benchmark datasets for SVM classification.