Accelerated gradient methods with absolute and relative noise in the gradient

On the Relative Efficiencies of Gradient Methods

A comparison is made among various gradient methods for maximizing a function, based on a characterization by Crockett and Chernoff of the class of these methods. By defining the "efficiency" of a gradient step in a certain way, it becomes easy to compare the efficiencies of different schemes with that of Newton's method, which can be regarded as a particular gradient scheme. For quadratic func...

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

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...

Gradient Convergence in Gradient methods with Errors

We consider the gradient method xt+1 = xt + γt(st + wt), where st is a descent direction of a function f : �n → � and wt is a deterministic or stochastic error. We assume that ∇f is Lipschitz continuous, that the stepsize γt diminishes to 0, and that st and wt satisfy standard conditions. We show that either f(xt) → −∞ or f(xt) converges to a finite value and ∇f(xt) → 0 (with probability 1 in t...