General Convergence Analysis of Stochastic First-Order Methods for Composite Optimization

Random Minibatch Subgradient Algorithms for Convex Problems with Functional Constraints

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Convergence of Stochastic Proximal Gradient Algorithm

(2019) Lorenzo Rosasco et al.   APPLIED MATHEMATICS AND OPTIMIZATION

Linear convergence of first order methods for non-strongly convex optimization

(2018) I. Necoara et al.   MATHEMATICAL PROGRAMMING

Optimization Methods for Large-Scale Machine Learning

(2018) Léon Bottou et al.   SIAM REVIEW

Computational Complexity of Inexact Gradient Augmented Lagrangian Methods: Application to Constrained MPC

(2014) Valentin Nedelcu et al.   SIAM JOURNAL ON CONTROL AND OPTIMIZATION

A Proximal Stochastic Gradient Method with Progressive Variance Reduction

(2014) Lin Xiao et al.   SIAM JOURNAL ON OPTIMIZATION

First-order methods of smooth convex optimization with inexact oracle

(2013) Olivier Devolder et al.   MATHEMATICAL PROGRAMMING

The solution path of the generalized lasso

(2011) Ryan J. Tibshirani et al.   ANNALS OF STATISTICS

Parallel and distributed optimization methods for estimation and control in networks

(2011) Ion Necoara et al.   JOURNAL OF PROCESS CONTROL

An optimal method for stochastic composite optimization

(2010) Guanghui Lan   MATHEMATICAL PROGRAMMING

Robust Stochastic Approximation Approach to Stochastic Programming

(2009) A. Nemirovski et al.   SIAM JOURNAL ON OPTIMIZATION