An Affine Invariant Linear Convergence Analysis for Frank-Wolfe Algorithms
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Abstract (arXiv)
We study the linear convergence of variants of the Frank-Wolfe algorithms for some classes of strongly convex problems, using only affine-invariant quantities. As in Guelat & Marcotte (1986), we show the linear convergence of the standard Frank-Wolfe algorithm when the solution is in the interior of the domain, but with affine invariant constants. We also show the linear convergence of the away-steps variant of the Frank-Wolfe algorithm, but with constants which only depend on the geometry of the domain, and not any property of the location of the optimal solution. Running these algorithms does not require knowing any problem specific parameters.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2013.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 2.
5 papers.
- 2023SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Banditscited by 3
- 2022Conditional Gradients for the Approximate Vanishing Idealcited by 1
- 2021Generating Structured Adversarial Attacks Using Frank-Wolfe Methodcited by 0
- 2025Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopescited by 0
- 2025Adaptive Conditional Gradient Descentcited by 0
- 2025Adaptive Conditional Gradient Descent13 Oct 2025
- 2025Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes16 May 2025
- 2023SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits29 Jan 2023
- 2022Conditional Gradients for the Approximate Vanishing Ideal7 Feb 2022
- 2021Generating Structured Adversarial Attacks Using Frank-Wolfe Method15 Feb 2021