Frank-Wolfe Algorithm
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The Frank–Wolfe algorithm is an iterative first-order optimization algorithm for constrained convex optimization. Also known as the conditional gradient method, reduced gradient algorithm and the convex combination algorithm.
Scientific Area:
Python
Language/Environments:
Optimization
Target Group:
Basic
Link:
Cite as:
Frank, M.; Wolfe, P. (1956). "An algorithm for quadratic programming". Naval Research Logistics Quarterly. 3 (1–2): 95–110. doi:10.1002/nav.3800030109.
Jaggi, Martin (2013). "Revisiting Frank–Wolfe: Projection-Free Sparse Convex Optimization". Journal of Machine Learning Research: Workshop and Conference Proceedings. 28 (1): 427–435. (Overview paper)
Jaggi, Martin (2013). "Revisiting Frank–Wolfe: Projection-Free Sparse Convex Optimization". Journal of Machine Learning Research: Workshop and Conference Proceedings. 28 (1): 427–435. (Overview paper)
Author of the review:
Ivo Nowak
HAW Hamburg
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Eligius Hendrix
The elegance of the algorithm is that it has been elaborated long time ago and for convex constrained problems, it is very effective.