# The Frank-Wolfe algorithm: a short introduction

arXiv: https://arxiv.org/abs/2311.05313 · HTML: https://mlsanity.com/paper/2311.05313/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2023.

## Built on by (most cited first, 9 of 9)

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- [Generalized Gradient Norm Clipping & Non-Euclidean (L0,L1)-Smoothness](https://mlsanity.com/paper/2506.01913/) (2025)
- [Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms](https://mlsanity.com/paper/2507.17545/) (2025)
- [Fair PCA, One Component at a Time](https://mlsanity.com/paper/2503.21563/) (2025)
- [Influential Bandits: Pulling an Arm May Change the Environment](https://mlsanity.com/paper/2504.08200/) (2025)
- [Quantum Algorithms for Projection-Free Sparse Convex Optimization](https://mlsanity.com/paper/2507.08543/) (2025)
- [Projection-Free CNN Pruning via Frank-Wolfe with Momentum: Sparser Models with Less Pretraining](https://mlsanity.com/paper/2512.01147/) (2025)
- [Empirical evaluation of the Frank-Wolfe methods for constructing white-box adversarial attacks](https://mlsanity.com/paper/2512.10936/) (2025)
- [Lloyd's K-Means Clustering Algorithm Is Frank-Wolfe in Disguise](https://mlsanity.com/paper/2607.25190/) (2026)

## Abstract (arXiv)

In this paper we provide an introduction to the Frank-Wolfe algorithm, a method for smooth convex optimization in the presence of (relatively) complicated constraints. We will present the algorithm, introduce key concepts, and establish important baseline results, such as e.g., primal and dual convergence. We will also discuss some of its properties, present a new adaptive step-size strategy as well as applications.