Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?

Hongchang Gao, Heng Huang
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3377-3386, 2020.

Abstract

Frank-Wolfe algorithm is an efficient method for optimizing non-convex constrained problems. However, most of existing methods focus on the first-order case. In real-world applications, the gradient is not always available. To address the problem of lacking gradient in many applications, we propose two new stochastic zeroth-order Frank-Wolfe algorithms and theoretically proved that they have a faster convergence rate than existing methods for non-convex problems. Specifically, the function queries oracle of the proposed faster zeroth-order Frank-Wolfe (FZFW) method is $O(\frac{n^{1/2}d}{\epsilon^2})$ which can match the iteration complexity of the first-order counterpart approximately. As for the proposed faster zeroth-order conditional gradient sliding (FZCGS) method, its function queries oracle is improved to $O(\frac{n^{1/2}d}{\epsilon})$, indicating that its iteration complexity is even better than that of its first-order counterpart NCGS-VR. In other words, the iteration complelxity of the accelerated first-order Frank-Wolfe method NCGS-VR is suboptimal. Then, we proposed a new algorithm to improve its IFO (incremental first-order oracle) to $O(\frac{n^{1/2}}{\epsilon})$. At last, the empirical studies on benchmark datasets validate our theoretical results.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-gao20b, title = {Can Stochastic Zeroth-Order Frank-{W}olfe Method Converge Faster for Non-Convex Problems?}, author = {Gao, Hongchang and Huang, Heng}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {3377--3386}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/gao20b/gao20b.pdf}, url = {http://proceedings.mlr.press/v119/gao20b.html}, abstract = {Frank-Wolfe algorithm is an efficient method for optimizing non-convex constrained problems. However, most of existing methods focus on the first-order case. In real-world applications, the gradient is not always available. To address the problem of lacking gradient in many applications, we propose two new stochastic zeroth-order Frank-Wolfe algorithms and theoretically proved that they have a faster convergence rate than existing methods for non-convex problems. Specifically, the function queries oracle of the proposed faster zeroth-order Frank-Wolfe (FZFW) method is $O(\frac{n^{1/2}d}{\epsilon^2})$ which can match the iteration complexity of the first-order counterpart approximately. As for the proposed faster zeroth-order conditional gradient sliding (FZCGS) method, its function queries oracle is improved to $O(\frac{n^{1/2}d}{\epsilon})$, indicating that its iteration complexity is even better than that of its first-order counterpart NCGS-VR. In other words, the iteration complelxity of the accelerated first-order Frank-Wolfe method NCGS-VR is suboptimal. Then, we proposed a new algorithm to improve its IFO (incremental first-order oracle) to $O(\frac{n^{1/2}}{\epsilon})$. At last, the empirical studies on benchmark datasets validate our theoretical results.} }
Endnote
%0 Conference Paper %T Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? %A Hongchang Gao %A Heng Huang %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-gao20b %I PMLR %P 3377--3386 %U http://proceedings.mlr.press/v119/gao20b.html %V 119 %X Frank-Wolfe algorithm is an efficient method for optimizing non-convex constrained problems. However, most of existing methods focus on the first-order case. In real-world applications, the gradient is not always available. To address the problem of lacking gradient in many applications, we propose two new stochastic zeroth-order Frank-Wolfe algorithms and theoretically proved that they have a faster convergence rate than existing methods for non-convex problems. Specifically, the function queries oracle of the proposed faster zeroth-order Frank-Wolfe (FZFW) method is $O(\frac{n^{1/2}d}{\epsilon^2})$ which can match the iteration complexity of the first-order counterpart approximately. As for the proposed faster zeroth-order conditional gradient sliding (FZCGS) method, its function queries oracle is improved to $O(\frac{n^{1/2}d}{\epsilon})$, indicating that its iteration complexity is even better than that of its first-order counterpart NCGS-VR. In other words, the iteration complelxity of the accelerated first-order Frank-Wolfe method NCGS-VR is suboptimal. Then, we proposed a new algorithm to improve its IFO (incremental first-order oracle) to $O(\frac{n^{1/2}}{\epsilon})$. At last, the empirical studies on benchmark datasets validate our theoretical results.
APA
Gao, H. & Huang, H.. (2020). Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:3377-3386 Available from http://proceedings.mlr.press/v119/gao20b.html.

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