Proximal gradient methods beyond monotony

Distributed Delayed Proximal Gradient Methods

We analyze distributed optimization algorithms where parts of data and variables are distributed over several machines and synchronization occurs asynchronously. We prove convergence for the general case of a nonconvex objective plus a convex and possibly nonsmooth penalty. We demonstrate two challenging applications, `1-regularized logistic regression and reconstruction ICA, and present experi...

Proximal extrapolated gradient methods for variational inequalities

The paper concerns with novel first-order methods for monotone variational inequalities. They use a very simple linesearch procedure that takes into account a local information of the operator. Also, the methods do not require Lipschitz continuity of the operator and the linesearch procedure uses only values of the operator. Moreover, when the operator is affine our linesearch becomes very simp...

Distributed Accelerated Proximal Coordinate Gradient Methods

We develop a general accelerated proximal coordinate descent algorithm in distributed settings (DisAPCG) for the optimization problem that minimizes the sum of two convex functions: the first part f is smooth with a gradient oracle, and the other one Ψ is separable with respect to blocks of coordinate and has a simple known structure (e.g., L1 norm). Our algorithm gets new accelerated convergen...

Accelerated Proximal Gradient Methods for Nonconvex Programming

Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine learning. However, solving the nonconvex and nonsmooth optimization problems remains a big challenge. Accelerated proximal gradient (APG) is an excellent method for convex programming. However, it is still unknown whether the usual APG can ensure the convergence to a...

Accelerated Proximal Gradient Methods for Nonconvex Programming