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An overview of Frank–Wolfe aka conditional gradients algorithms, including references to papers and codes.
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Conditional Gradient Methods Conditional Gradient Methods The purpose of our survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank–Wolfe algorithms, also called conditional gradient algorithms, for function minimization. These are first-order algorithms accessing the feasible region only through linear minimization, and are especially useful in convex optimization, in particular when linear optimization is cheaper than projection. The selection of the material has been guided by the principle of highlighting crucial ideas as well as presenting new approaches that we believe might become important in the future, with ample citations even of old works imperative in the development of newer methods. Yet, our selection is sometimes biased, for example all applications are from machine learning, and need not reflect consensus of the research community. We have certainly missed recent important contributions. After all the research area o...
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