Pages that link to "Item:Q5361273"

The following pages link to On perturbed proximal gradient algorithms (Q5361273):

Displaying 42 items.

• Stochastic forward-backward splitting for monotone inclusions (Q289110) (← links)
• Exploiting multi-core architectures for reduced-variance estimation with intractable likelihoods (Q516453) (← links)
• Dynamical behavior of a stochastic forward-backward algorithm using random monotone operators (Q727220) (← links)
• A fully stochastic primal-dual algorithm (Q828693) (← links)
• A SAEM algorithm for fused Lasso penalized nonlinear mixed effect models: application to group comparison in pharmacokinetics (Q1659496) (← links)
• On variance reduction for stochastic smooth convex optimization with multiplicative noise (Q1739038) (← links)
• Stochastic quasi-Fejér block-coordinate fixed point iterations with random sweeping. II: Mean-square and linear convergence (Q1739044) (← links)
• On the proximal gradient algorithm with alternated inertia (Q1752647) (← links)
• Convergence of contrastive divergence algorithm in exponential family (Q1991693) (← links)
• Convergence of stochastic proximal gradient algorithm (Q2019902) (← links)
• A unified convergence analysis of stochastic Bregman proximal gradient and extragradient methods (Q2031928) (← links)
• General convergence analysis of stochastic first-order methods for composite optimization (Q2032020) (← links)
• Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation (Q2058738) (← links)
• Consistent online Gaussian process regression without the sample complexity bottleneck (Q2058904) (← links)
• Fast selection of nonlinear mixed effect models using penalized likelihood (Q2072413) (← links)
• Convergence analysis of the stochastic reflected forward-backward splitting algorithm (Q2091216) (← links)
• High-performance statistical computing in the computing environments of the 2020s (Q2092893) (← links)
• Perturbed iterate SGD for Lipschitz continuous loss functions (Q2093279) (← links)
• SABRINA: a stochastic subspace majorization-minimization algorithm (Q2095568) (← links)
• Computation for latent variable model estimation: a unified stochastic proximal framework (Q2103576) (← links)
• Stochastic proximal-gradient algorithms for penalized mixed models (Q2329762) (← links)
• A random block-coordinate Douglas-Rachford splitting method with low computational complexity for binary logistic regression (Q2419533) (← links)
• Ergodic convergence of a stochastic proximal point algorithm (Q2828340) (← links)
• Stability of Over-Relaxations for the Forward-Backward Algorithm, Application to FISTA (Q3454513) (← links)
• (Q4558148) (← links)
• Nonasymptotic convergence of stochastic proximal point algorithms for constrained convex optimization (Q4558525) (← links)
• (Q4614120) (← links)
• Optimization Methods for Large-Scale Machine Learning (Q4641709) (← links)
• (Q4967840) (← links)
• Strong convergence of over-relaxed multi-parameter proximal scaled gradient algorithm and superiorization (Q4986410) (← links)
• Proximal Gradient Methods for Machine Learning and Imaging (Q5028165) (← links)
• Gradient flows and randomised thresholding: sparse inversion and classification* (Q5058108) (← links)
• Minibatch Forward-Backward-Forward Methods for Solving Stochastic Variational Inequalities (Q5084485) (← links)
• The Stochastic Auxiliary Problem Principle in Banach Spaces: Measurability and Convergence (Q5097016) (← links)
• Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments (Q5143322) (← links)
• Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis (Q5143323) (← links)
• A dual-based stochastic inexact algorithm for a class of stochastic nonsmooth convex composite problems (Q6051310) (← links)
• A strong law of large numbers for random monotone operators (Q6084858) (← links)
• Unified analysis of stochastic gradient methods for composite convex and smooth optimization (Q6086133) (← links)
• On the accept-reject mechanism for Metropolis-Hastings algorithms (Q6139681) (← links)
• Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization (Q6172923) (← links)
• The stochastic proximal distance algorithm (Q6657820) (← links)