NeurIPS 2019poster16 citations
Primal-Dual Block Generalized Frank-Wolfe
Qi Lei, JIACHENG ZHUO, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis
Abstract
We propose a generalized variant of 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 Generalized 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.
BibTeX
@inproceedings{NEURIPS2019_fb03a0f2,
author = {Lei, Qi and ZHUO, JIACHENG and Caramanis, Constantine and Dhillon, Inderjit S and Dimakis, Alexandros G},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Primal-Dual Block Generalized Frank-Wolfe},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/fb03a0f2f5d94af4a5c0890fff0ef6e0-Paper.pdf},
volume = {32},
year = {2019}
}