Linear Convergence of Adaptive Stochastic Gradient Descent

Yuege Xie, Xiaoxia Wu, Rachel Ward
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1475-1485, 2020.

Abstract

We prove that the norm version of the adaptive stochastic gradient method (AdaGrad-Norm) achieves a linear convergence rate for a subset of either strongly convex functions or non-convex functions that satisfy the Polyak Lojasiewicz (PL) inequality. The paper introduces the notion of Restricted Uniform Inequality of Gradients (RUIG)—which is a measure of the balanced-ness of the stochastic gradient norms—to depict the landscape of a function. RUIG plays a key role in proving the robustness of AdaGrad-Norm to its hyper-parameter tuning in the stochastic setting. On top of RUIG, we develop a two-stage framework to prove the linear convergence of AdaGrad-Norm without knowing the parameters of the objective functions. This framework can likely be extended to other adaptive stepsize algorithms. The numerical experiments validate the theory and suggest future directions for improvement.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-xie20a, title = {Linear Convergence of Adaptive Stochastic Gradient Descent}, author = {Xie, Yuege and Wu, Xiaoxia and Ward, Rachel}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {1475--1485}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/xie20a/xie20a.pdf}, url = { http://proceedings.mlr.press/v108/xie20a.html }, abstract = {We prove that the norm version of the adaptive stochastic gradient method (AdaGrad-Norm) achieves a linear convergence rate for a subset of either strongly convex functions or non-convex functions that satisfy the Polyak Lojasiewicz (PL) inequality. The paper introduces the notion of Restricted Uniform Inequality of Gradients (RUIG)—which is a measure of the balanced-ness of the stochastic gradient norms—to depict the landscape of a function. RUIG plays a key role in proving the robustness of AdaGrad-Norm to its hyper-parameter tuning in the stochastic setting. On top of RUIG, we develop a two-stage framework to prove the linear convergence of AdaGrad-Norm without knowing the parameters of the objective functions. This framework can likely be extended to other adaptive stepsize algorithms. The numerical experiments validate the theory and suggest future directions for improvement.} }
Endnote
%0 Conference Paper %T Linear Convergence of Adaptive Stochastic Gradient Descent %A Yuege Xie %A Xiaoxia Wu %A Rachel Ward %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-xie20a %I PMLR %P 1475--1485 %U http://proceedings.mlr.press/v108/xie20a.html %V 108 %X We prove that the norm version of the adaptive stochastic gradient method (AdaGrad-Norm) achieves a linear convergence rate for a subset of either strongly convex functions or non-convex functions that satisfy the Polyak Lojasiewicz (PL) inequality. The paper introduces the notion of Restricted Uniform Inequality of Gradients (RUIG)—which is a measure of the balanced-ness of the stochastic gradient norms—to depict the landscape of a function. RUIG plays a key role in proving the robustness of AdaGrad-Norm to its hyper-parameter tuning in the stochastic setting. On top of RUIG, we develop a two-stage framework to prove the linear convergence of AdaGrad-Norm without knowing the parameters of the objective functions. This framework can likely be extended to other adaptive stepsize algorithms. The numerical experiments validate the theory and suggest future directions for improvement.
APA
Xie, Y., Wu, X. & Ward, R.. (2020). Linear Convergence of Adaptive Stochastic Gradient Descent. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1475-1485 Available from http://proceedings.mlr.press/v108/xie20a.html .

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