Search Results - stochastic variance-reduced algorithm

A variance‐reduced distributed stochastic momentum algorithm over directed networks by Gong, Xiuhui, Gao, Juan, Liu, Xinwei

“…‐reduced distributed stochastic momentum algorithm over directed networks, named DSM‐SAGA, is developed. DSM‐SAGA is a combination of the heavy…”
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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization by Jiang, Xia, Zhu, Linglingzhi, Zheng, Taoli, So, Anthony Man-Cho

“… In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm…”
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Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization by Zhang, Junyu, Xiao, Lin

“… We propose a class of stochastic variance-reduced prox-linear algorithms for solving such problems and bound their sample complexities for finding an ϵ…”
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A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data by Shajoonnezhad, Nazanin, Nikanjam, Amin

“… Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function…”
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Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds by Zhou, Pan, Yuan, Xiao-Tong, Yan, Shuicheng, Feng, Jiashi

“…) algorithm to solve the finite-sum and online Riemannian non-convex minimization problems. At the core of R-SPIDER is a recursive semi-stochastic gradient estimator…”
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A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization by 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…”
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Two fast variance-reduced proximal gradient algorithms for SMVIPs-Stochastic Mixed Variational Inequality Problems with suitable applications to stochastic network games and traffic assignment problems by Yang, Zhen-Ping, Lin, Gui-Hua

“…In this paper, we propose two proximal gradient algorithms with variance reduction for stochastic mixed variational inequality problems…”
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Communication-Efficient Federated Learning: A Variance-Reduced Stochastic Approach With Adaptive Sparsification by Wang, Bin, Fang, Jun, Li, Hongbin, Zeng, Bing

“…Federated learning (FL) is an emerging distributed machine learning paradigm that aims to realize model training without gathering the data from data sources…”
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Accelerated variance-reduced methods for saddle-point problems by Borodich, Ekaterina, Tominin, Vladislav, Tominin, Yaroslav, Kovalev, Dmitry, Gasnikov, Alexander, Dvurechensky, Pavel

“… for the primal and dual variables. For such problems, under the average-smoothness assumption, we propose accelerated stochastic variance-reduced algorithms with optimal up to logarithmic factors complexity bounds…”
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Stochastic variance reduced gradient with hyper-gradient for non-convex large-scale learning by Yang, Zhuang

“… With faster convergence rate, there have been tremendous studies on developing stochastic variance reduced algorithms to solve these non-convex optimization problems…”
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Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods by Morin, Martin, Giselsson, Pontus

“…With the purpose of examining biased updates in variance-reduced stochastic gradient methods, we introduce SVAG, a SAG/SAGA-like method with adjustable bias…”
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Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications by Liu, Yuanyuan, Geng, Jiacheng, Shang, Fanhua, An, Weixin, Liu, Hongying, Zhu, Qi

“… Recently, several stochastic variance reduced ADMM algorithms (e.g., SVRG-ADMM) have made exciting progress, such as linear convergence for strongly convex (SC) problems…”
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Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport by Sato, Hiroyuki, Kasai, Hiroyuki, Mishra, Bamdev

“… This paper proposes a novel Riemannian extension of the Euclidean stochastic variance reduced gradient (R-SVRG…”
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A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates by Zhou, Kaiwen, Shang, Fanhua, Cheng, James

“…, sparse and asynchronous) due to the existence of perturbation. In this paper, we introduce a simple stochastic variance reduced algorithm (MiG…”
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Stochastic Variance-Reduced Majorization-Minimization Algorithms by Phan, Duy-Nhat, Bartz, Sedi, Guha, Nilabja, Phan, Hung M

“… Consequently, effective algorithms for such scenarios are scarce. We introduce and study three stochastic variance-reduced majorization-minimization (MM…”
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Variance Reduced Stochastic Proximal Algorithm for AUC Maximization by Soham Dan, Sahoo, Dushyant

“… However, these stochastic algorithms cannot be directly used when non-decomposable pairwise performance measures are used such as Area under the ROC curve (AUC…”
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SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities by Beznosikov, Aleksandr, Gasnikov, Alexander

“… Motivated by applications in machine learning and beyond, stochastic methods are of great importance…”
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SARAH-M: A fast stochastic recursive gradient descent algorithm via momentum by Yang, Zhuang

“… However, the understanding of how the momentum improves the performance of modern variance reduced stochastic gradient algorithms, e.g…”
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Riemannian Stochastic Variance-Reduced Cubic Regularized Newton Method for Submanifold Optimization by Zhang, Dewei, Davanloo Tajbakhsh, Sam

“…We propose a stochastic variance-reduced cubic regularized Newton algorithm to optimize the finite-sum problem over a Riemannian submanifold of the Euclidean space…”
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Hybrid Acceleration Scheme for Variance Reduced Stochastic Optimization Algorithms by Sadeghi, Hamed, Giselsson, Pontus

“… To alleviate this problem, we propose an optimization algorithm -- which we refer to as a hybrid acceleration scheme -- for a class of proximal variance reduced stochastic optimization algorithms…”
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