mlsanityData to 7 Oct 2026

New Analysis and Results for the Frank-Wolfe Method

arXiv 1307.0873 ↗First posted 2 Jul 2013math.OCcited by 17

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Abstract (arXiv)

We present new results for the Frank-Wolfe method (also known as the conditional gradient method). We derive computational guarantees for arbitrary step-size sequences, which are then applied to various step-size rules, including simple averaging and constant step-sizes. We also develop step-size rules and computational guarantees that depend naturally on the warm-start quality of the initial (and subsequent) iterates. Our results include computational guarantees for both duality/bound gaps and the so-called FW gaps. Lastly, we present complexity bounds in the presence of approximate computation of gradients and/or linear optimization subproblem solutions.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 3; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

17 papers.

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  1. 2021No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimizationcited by 20
  2. 2021Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyondcited by 10
  3. 2021Frank-Wolfe with a Nearest Extreme Point Oraclecited by 8
  4. 2021Heavy Ball Momentum for Conditional Gradientcited by 7
  5. 2022Curriculum-based Asymmetric Multi-task Reinforcement Learningcited by 7
  6. 2021Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approachcited by 6
  7. 2021First-Order Methods for Convex Optimizationcited by 4
  8. 2024Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approachcited by 2
  9. 2022A Multistep Frank-Wolfe Methodcited by 1
  10. 2023Responsible AI (RAI) Games and Ensemblescited by 1
  11. 2026Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizescited by 1
  12. 2021Approximate Frank-Wolfe Algorithms over Graph-structured Support Setscited by 0
5 more
  1. 2022A conditional gradient homotopy method with applications to Semidefinite Programmingcited by 0
  2. 2023On Finite-Step Convergence of the Non-Greedy Algorithm and Proximal Alternating Minimization Method with Extrapolation for L1-Norm PCAcited by 0
  3. 2023Reducing Discretization Error in the Frank-Wolfe Methodcited by 0
  4. 2023Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphscited by 0
  5. 2024Federated Frank-Wolfe Algorithmcited by 0