mlsanityData to 7 Oct 2026

Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls

arXiv 1708.02105 ↗First posted 7 Aug 2017cs.LG cs.DS math.OCcited by 15

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Abstract (arXiv)

We propose a rank-k variant of the classical Frank-Wolfe algorithm to solve convex optimization over a trace-norm ball. Our algorithm replaces the top singular-vector computation (1-SVD) in Frank-Wolfe with a top-k singular-vector computation (k-SVD), which can be done by repeatedly applying 1-SVD k times. Alternatively, our algorithm can be viewed as a rank-k restricted version of projected gradient descent. We show that our algorithm has a linear convergence rate when the objective function is smooth and strongly convex, and the optimal solution has rank at most k. This improves the convergence rate and the total time complexity of the Frank-Wolfe method and its variants.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2017.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 0; 2024: 1; 2025: 5; 2026: 1.

15 papers.

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