mlsanityData to 14 Oct 2026

FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients

arXiv 2104.06675 ↗First posted 14 Apr 2021math.OCcited by 8

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Abstract (arXiv)

We present FrankWolfe.jl, an open-source implementation of several popular Frank-Wolfe and Conditional Gradients variants for first-order constrained optimization. The package is designed with flexibility and high-performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia's unique multiple dispatch feature, and interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2021.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 0; 2024: 0; 2025: 2.

8 papers.

  1. 2022Learning with Combinatorial Optimization Layers: a Probabilistic Approachcited by 29
  2. 2021Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderingscited by 5
  3. 2022Convex mixed-integer optimization with Frank-Wolfe methodscited by 3
  4. 2022SAIF: Sparse Adversarial and Imperceptible Attack Frameworkcited by 2
  5. 2021Scalable Frank-Wolfe on Generalized Self-concordant Functions via Simple Stepscited by 1
  6. 2022Accelerated Nonnegative Tensor Completion via Integer Programmingcited by 1
  7. 2025Scalable DC Optimization via Adaptive Frank-Wolfe Algorithmscited by 1
  8. 2025An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimizationcited by 0