mlsanityData to 7 Oct 2026

The Frank-Wolfe algorithm: a short introduction

arXiv 2311.05313 ↗First posted 9 Nov 2023math.OCcited by 9

Not on the map. Why?

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.

  1. 2025Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)cited by 66
  2. 2025Generalized Gradient Norm Clipping & Non-Euclidean (L0,L1)-Smoothnesscited by 17
  3. 2025Scalable DC Optimization via Adaptive Frank-Wolfe Algorithmscited by 1
  4. 2025Fair PCA, One Component at a Timecited by 0
  5. 2025Influential Bandits: Pulling an Arm May Change the Environmentcited by 0
  6. 2025Quantum Algorithms for Projection-Free Sparse Convex Optimizationcited by 0
  7. 2025Projection-Free CNN Pruning via Frank-Wolfe with Momentum: Sparser Models with Less Pretrainingcited by 0
  8. 2025Empirical evaluation of the Frank-Wolfe methods for constructing white-box adversarial attackscited by 0
  9. 2026Lloyd's K-Means Clustering Algorithm Is Frank-Wolfe in Disguisecited by 0