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arXiv · 1906.02436

Primal-Dual Block Frank-Wolfe

Qi Lei·Jiacheng Zhuo·Constantine Caramanis·Inderjit S. Dhillon·Alexandros G. Dimakis
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Abstract

We propose a variant of the Frank-Wolfe algorithm for solving a class of sparse/low-rank optimization problems. Our formulation includes Elastic Net, regularized SVMs and phase retrieval as special cases. The proposed Primal-Dual Block Frank-Wolfe algorithm reduces the per-iteration cost while maintaining linear convergence rate. The per iteration cost of our method depends on the structural complexity of the solution (i.e. sparsity/low-rank) instead of the ambient dimension. We empirically show that our algorithm outperforms the state-of-the-art methods on (multi-class) classification tasks.

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Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis. 2019-06-06. Primal-Dual Block Frank-Wolfe. https://arxiv.org/abs/1906.02436

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