Suykens, “Hybrid conditional gradient-smoothing algorithms with applications to sparse and low rank regularization

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Suykens, “Hybrid conditional gradient-smoothing algorithms with applications to sparse and low rank regularization (2014)

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[arxiv.org]

by A. Argyriou , M. Signoretto , J. Suykens

Venue:	Regularization, Optimization, Kernels, and Support Vector Machines
Citations:	1 - 0 self

BibTeX

@INPROCEEDINGS{Argyriou14suykens,“hybrid,
    author = {A. Argyriou and M. Signoretto and J. Suykens},
    title = {Suykens, “Hybrid conditional gradient-smoothing algorithms with applications to sparse and low rank regularization},
    booktitle = {Regularization, Optimization, Kernels, and Support Vector Machines},
    year = {2014},
    pages = {53}
}

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Abstract

Conditional gradient methods are old and well studied optimization algorithms. Their origin dates at least to the 50’s and the Frank-Wolfe algorithm for quadratic programming [18] but they apply to much more general optimization problems. General formulations of conditional gradient algorithms have been studied in the

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