# Simplified Versions of the Conditional Gradient Method

arXiv: https://arxiv.org/abs/1801.05251 · HTML: https://mlsanity.com/paper/1801.05251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

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

- [Self-adaptive algorithms for quasiconvex programming and applications to machine learning](https://mlsanity.com/paper/2212.06379/) (2022)
- [Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning](https://mlsanity.com/paper/2402.06224/) (2024)

## Abstract (arXiv)

We suggest simple modifications of the conditional gradient method for smooth optimization problems, which maintain the basic convergence properties, but reduce the implementation cost of each iteration essentially. Namely, we propose the step-size procedure without any line-search, and inexact solution of the direction finding subproblem. Preliminary results of computational tests confirm efficiency of the proposed modifications.