A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradie...

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Main Authors: ZHOU, Pan, YUAN, Xiao-Tong, LIN Zhouchen, HOI, Steven C. H.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/8979
https://ink.library.smu.edu.sg/context/sis_research/article/9982/viewcontent/2021_TPAMI_HSDN.pdf
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spelling sg-smu-ink.sis_research-99822024-07-25T08:32:47Z A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization ZHOU, Pan YUAN, Xiao-Tong LIN Zhouchen, HOI, Steven C. H. Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g. least squares and logistic/softmax regression. HSDMPG enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of n components, HSDMPG attains an -optimization-error within O κ logζ+1 1 1 V n logζ 1 stochastic gradient evaluations, where κ is condition number, ζ = 1 for quadratic loss and ζ = 2 for generic loss. For large-scale problems, our complexity outperforms those of SVRG-type algorithms with/without dependence on data size. Particularly, when = O(1/ √ n) which matches the intrinsic excess error of a learning model and is sufficient for generalization, our complexity for quadratic and generic losses is respectively O(n 0.5 log2 (n)) and O(n 0.5 log3 (n)), which for the first time achieves optimal generalization in less than a single pass over data. Besides, we extend HSDMPG to online strongly convex problems and prove its higher efficiency over the prior algorithms. Numerical results demonstrate the computational advantages of HSDM. 2021-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8979 info:doi/10.1109/TPAMI.2021.3087328 https://ink.library.smu.edu.sg/context/sis_research/article/9982/viewcontent/2021_TPAMI_HSDN.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Convex Optimization Precondition Online Convex Optimization Stochastic Variance-Reduced Algorithm Artificial Intelligence and Robotics Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Convex Optimization
Precondition
Online Convex Optimization
Stochastic Variance-Reduced Algorithm
Artificial Intelligence and Robotics
Theory and Algorithms
spellingShingle Convex Optimization
Precondition
Online Convex Optimization
Stochastic Variance-Reduced Algorithm
Artificial Intelligence and Robotics
Theory and Algorithms
ZHOU, Pan
YUAN, Xiao-Tong
LIN Zhouchen,
HOI, Steven C. H.
A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
description Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g. least squares and logistic/softmax regression. HSDMPG enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of n components, HSDMPG attains an -optimization-error within O κ logζ+1 1 1 V n logζ 1 stochastic gradient evaluations, where κ is condition number, ζ = 1 for quadratic loss and ζ = 2 for generic loss. For large-scale problems, our complexity outperforms those of SVRG-type algorithms with/without dependence on data size. Particularly, when = O(1/ √ n) which matches the intrinsic excess error of a learning model and is sufficient for generalization, our complexity for quadratic and generic losses is respectively O(n 0.5 log2 (n)) and O(n 0.5 log3 (n)), which for the first time achieves optimal generalization in less than a single pass over data. Besides, we extend HSDMPG to online strongly convex problems and prove its higher efficiency over the prior algorithms. Numerical results demonstrate the computational advantages of HSDM.
format text
author ZHOU, Pan
YUAN, Xiao-Tong
LIN Zhouchen,
HOI, Steven C. H.
author_facet ZHOU, Pan
YUAN, Xiao-Tong
LIN Zhouchen,
HOI, Steven C. H.
author_sort ZHOU, Pan
title A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
title_short A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
title_full A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
title_fullStr A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
title_full_unstemmed A hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
title_sort hybrid stochastic-deterministic minibatch proximal gradient method for efficient optimization and generalization
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/8979
https://ink.library.smu.edu.sg/context/sis_research/article/9982/viewcontent/2021_TPAMI_HSDN.pdf
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