Primal-Dual Block Frank-Wolfe
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Publication:6320074
arXiv1906.02436MaRDI QIDQ6320074
Qi Lei, Jiacheng Zhuo, Alexandros G. Dimakis, Inderjit S. Dhillon, Constantine Caramanis
Publication date: 6 June 2019
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.
Has companion code repository: https://github.com/CarlsonZhuo/primal_dual_frank_wolfe
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