Natasha: Faster stochastic non-convex optimization via strongly non-convex parameter, 2016. ,
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009. ,
DOI : 10.1137/080716542
Nonlinear programming, Athena scientific Belmont, 1999. ,
Convex Optimization Algorithms, Athena Scientific, 2015. ,
From error bounds to the complexity of first-order descent methods for convex functions, Mathematical Programming, 2016. ,
DOI : 10.1007/s10107-016-1091-6
Convex analysis and nonlinear optimization: Theory and examples, 2006. ,
Accelerated methods for non-convex optimization, 2016. ,
Convex until proven guilty " : Dimension-free acceleration of gradient descent on non-convex functions, 2017. ,
On the complexity of finding first-order critical points in constrained nonlinear optimization, Mathematical Programming, 2014. ,
On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems, SIAM Journal on Optimization, vol.20, issue.6, pp.2833-2852, 2010. ,
DOI : 10.1137/090774100
Proximal smoothness and the lower-C 2 property, Journal of Convex Analysis, vol.2, issue.12, pp.117-144, 1995. ,
SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives, Advances in Neural Information Processing Systems (NIPS), 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01016843
Efficiency of minimizing compositions of convex functions and smooth maps, 2016. ,
Curvature measures. Transactions of the, pp.418-491, 1959. ,
DOI : 10.2307/1993504
Accelerated gradient methods for nonconvex nonlinear and stochastic programming, Mathematical Programming, pp.59-99, 2016. ,
DOI : 10.1007/s10107-015-0871-8
URL : http://arxiv.org/abs/1310.3787
Generalized uniformly optimal methods for nonlinear programming, 2015. ,
On the Convergence of the Proximal Point Algorithm for Convex Minimization, SIAM Journal on Control and Optimization, vol.29, issue.2, pp.403-419, 1991. ,
DOI : 10.1137/0329022
Statistical Learning With Sparsity: The Lasso And Generalizations, 2015. ,
Accelerating stochastic gradient descent using predictive variance reduction, Advances in Neural Information Processing Systems (NIPS), 2013. ,
An optimal randomized incremental gradient method, 2015. ,
Accelerated proximal gradient methods for nonconvex programming, Advances in Neural Information Processing Systems (NIPS), 2015. ,
A universal catalyst for first-order optimization, Advances in Neural Information Processing Systems (NIPS), 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01160728
Sparse modeling for image and vision processing. Foundations and Trends in Computer Graphics and Vision, pp.85-283, 2014. ,
DOI : 10.1561/0600000058
URL : https://hal.archives-ouvertes.fr/hal-01081139
Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research (JMLR), vol.11, pp.19-60, 2010. ,
URL : https://hal.archives-ouvertes.fr/inria-00408716
A method of solving a convex programming problem with convergence rate, Soviet Mathematics Doklady, vol.27, issue.1 22, pp.372-376, 1983. ,
Introductory Lectures on Convex Optimization: A Basic Course, 2004. ,
DOI : 10.1007/978-1-4419-8853-9
How to make the gradients small. OPTIMA, MPS Newsletter, issue.88, pp.10-11, 2012. ,
Gradient methods for minimizing composite functions, Mathematical Programming, pp.125-161, 2013. ,
DOI : 10.1007/s10107-012-0629-5
Proximal Algorithms, Foundations and Trends?? in Optimization, vol.1, issue.3, pp.123-231, 2014. ,
DOI : 10.1561/2400000003
Prox-regular functions in variational analysis. Transactions of the, pp.1805-1838, 1996. ,
Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization, Advances in Neural Information Processing Systems (NIPS), 2016. ,
Favorable classes of Lipschitz-continuous functions in subgradient optimization, Progress in nondifferentiable optimization of IIASA Collaborative Proc. Ser. CP-82, pp.125-143, 1982. ,
Variational analysis, of Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences, 1998. ,
DOI : 10.1007/978-3-642-02431-3
Minimizing finite sums with the stochastic average gradient, Mathematical Programming, vol.24, issue.2, pp.83-112, 2017. ,
DOI : 10.1007/s10107-016-1030-6
URL : https://hal.archives-ouvertes.fr/hal-00860051
On accelerated proximal gradient methods for convex-concave optimization, 2008. ,
Tight complexity bounds for optimizing composite objectives, Advances in Neural Information Processing Systems (NIPS), 2016. ,
A Proximal Stochastic Gradient Method with Progressive Variance Reduction, SIAM Journal on Optimization, vol.24, issue.4, pp.2057-2075, 2014. ,
DOI : 10.1137/140961791
URL : http://arxiv.org/abs/1403.4699
Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005. ,
DOI : 10.1073/pnas.201162998
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.4696