Pages that link to "Item:Q5444279"

The following pages link to A Convergent Incremental Gradient Method with a Constant Step Size (Q5444279):

Displaying 50 items.

• New strong convergence theorems for split variational inclusion problems in Hilbert spaces (Q264502) (← links)
• An incremental decomposition method for unconstrained optimization (Q272371) (← links)
• Distributed Nash equilibrium seeking: a gossip-based algorithm (Q311956) (← links)
• Convergence analysis of iterative methods for nonsmooth convex optimization over fixed point sets of quasi-nonexpansive mappings (Q312694) (← links)
• Minimizing finite sums with the stochastic average gradient (Q517295) (← links)
• Approximation accuracy, gradient methods, and error bound for structured convex optimization (Q607498) (← links)
• Distributed stochastic subgradient projection algorithms for convex optimization (Q620442) (← links)
• Incremental proximal methods for large scale convex optimization (Q644913) (← links)
• Generalized row-action methods for tomographic imaging (Q742852) (← links)
• Distributed multi-task classification: a decentralized online learning approach (Q1640564) (← links)
• Convergence of stochastic proximal gradient algorithm (Q2019902) (← links)
• Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods (Q2023684) (← links)
• Variable smoothing incremental aggregated gradient method for nonsmooth nonconvex regularized optimization (Q2047203) (← links)
• Inertial proximal incremental aggregated gradient method with linear convergence guarantees (Q2084299) (← links)
• On the convergence of a block-coordinate incremental gradient method (Q2100401) (← links)
• Subsampled nonmonotone spectral gradient methods (Q2178981) (← links)
• Accelerating incremental gradient optimization with curvature information (Q2181597) (← links)
• Linear convergence of cyclic SAGA (Q2193004) (← links)
• Hierarchical constrained consensus algorithm over multi-cluster networks (Q2200562) (← links)
• An incremental aggregated proximal ADMM for linearly constrained nonconvex optimization with application to sparse logistic regression problems (Q2226322) (← links)
• Primal-dual incremental gradient method for nonsmooth and convex optimization problems (Q2230784) (← links)
• Incrementally updated gradient methods for constrained and regularized optimization (Q2251572) (← links)
• Primal-dual stochastic distributed algorithm for constrained convex optimization (Q2334189) (← links)
• A globally convergent incremental Newton method (Q2349125) (← links)
• Restricted strong convexity and its applications to convergence analysis of gradient-type methods in convex optimization (Q2355319) (← links)
• A framework for parallel second order incremental optimization algorithms for solving partially separable problems (Q2419531) (← links)
• Multi-cluster distributed optimization via random sleep strategy (Q2423910) (← links)
• Incremental subgradient method for nonsmooth convex optimization with fixed point constraints (Q2829569) (← links)
• On perturbed steepest descent methods with inexact line search for bilevel convex optimization (Q3112499) (← links)
• String-averaging incremental stochastic subgradient algorithms (Q4631774) (← links)
• Algorithms and Convergence Theorems for Mixed Equilibrium Problems in Hilbert Spaces (Q4631914) (← links)
• Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization (Q4636997) (← links)
• Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods (Q4641660) (← links)
• Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate (Q4641666) (← links)
• Optimization Methods for Large-Scale Machine Learning (Q4641709) (← links)
• The Averaged Kaczmarz Iteration for Solving Inverse Problems (Q4686928) (← links)
• Random Gradient Extrapolation for Distributed and Stochastic Optimization (Q4687240) (← links)
• Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications (Q4687241) (← links)
• GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (Q4969135) (← links)
• Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence Under Bregman Distance Growth Conditions (Q4991666) (← links)
• A randomized incremental primal-dual method for decentralized consensus optimization (Q4995044) (← links)
• Incremental proximal gradient scheme with penalization for constrained composite convex optimization problems (Q4999758) (← links)
• Stochastic average gradient algorithm for multirate FIR models with varying time delays using self‐organizing maps (Q5003430) (← links)
• Proximal Gradient Methods for Machine Learning and Imaging (Q5028165) (← links)
• Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling (Q5072595) (← links)
• On the Efficiency of Random Permutation for ADMM and Coordinate Descent (Q5108265) (← links)
• Convergence Rate of Incremental Gradient and Incremental Newton Methods (Q5237308) (← links)
• Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning (Q5254990) (← links)
• On the Convergence Rate of Incremental Aggregated Gradient Algorithms (Q5266533) (← links)
• IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate (Q5745078) (← links)