Výsledky vyhledávání - stochastic variance-reduced algorithm

A variance‐reduced distributed stochastic momentum algorithm over directed networks Autor 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…”
Získat plný text

Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization Autor 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…”
Získat plný text

Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization Autor 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…”
Získat plný text

A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data Autor Shajoonnezhad, Nazanin, Nikanjam, Amin

“… Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function…”
Získat plný text

Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds Autor 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…”
Získat plný text

A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization Autor 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…”
Získat plný text

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 Autor Yang, Zhen-Ping, Lin, Gui-Hua

“…In this paper, we propose two proximal gradient algorithms with variance reduction for stochastic mixed variational inequality problems…”
Získat plný text

Communication-Efficient Federated Learning: A Variance-Reduced Stochastic Approach With Adaptive Sparsification Autor 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…”
Získat plný text

Accelerated variance-reduced methods for saddle-point problems Autor 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…”
Získat plný text

Stochastic variance reduced gradient with hyper-gradient for non-convex large-scale learning Autor Yang, Zhuang

“… With faster convergence rate, there have been tremendous studies on developing stochastic variance reduced algorithms to solve these non-convex optimization problems…”
Získat plný text

Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods Autor 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…”
Získat plný text

Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications Autor 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…”
Získat plný text

Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport Autor Sato, Hiroyuki, Kasai, Hiroyuki, Mishra, Bamdev

“… This paper proposes a novel Riemannian extension of the Euclidean stochastic variance reduced gradient (R-SVRG…”
Získat plný text

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Autor 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…”
Získat plný text

Stochastic Variance-Reduced Majorization-Minimization Algorithms Autor 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…”
Získat plný text

Variance Reduced Stochastic Proximal Algorithm for AUC Maximization Autor 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…”
Získat plný text

SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities Autor Beznosikov, Aleksandr, Gasnikov, Alexander

“… Motivated by applications in machine learning and beyond, stochastic methods are of great importance…”
Získat plný text

SARAH-M: A fast stochastic recursive gradient descent algorithm via momentum Autor Yang, Zhuang

“… However, the understanding of how the momentum improves the performance of modern variance reduced stochastic gradient algorithms, e.g…”
Získat plný text

Riemannian Stochastic Variance-Reduced Cubic Regularized Newton Method for Submanifold Optimization Autor 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…”
Získat plný text

Hybrid Acceleration Scheme for Variance Reduced Stochastic Optimization Algorithms Autor 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…”
Získat plný text