Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

This article presents a powerful algorithmic framework for big data optimization, called the
block successive upper-bound minimization (BSUM). The BSUM includes as special cases …

This paper introduces a parallel and distributed algorithm for solving the following
minimization problem with linear constraints: minimize~~ &f_1 (x _1)+ ⋯+ f_N (x _N)\subject …

In this work we show that randomized (block) coordinate descent methods can be
accelerated by parallelization when applied to the problem of minimizing the sum of a …

We describe an asynchronous parallel stochastic coordinate descent algorithm for
minimizing smooth unconstrained or separably constrained functions. The method achieves …

Finding a fixed point to a nonexpansive operator, ie, x^*=Tx^*, abstracts many problems in
numerical linear algebra, optimization, and other areas of data science. To solve fixed-point …

The block coordinate descent (BCD) method is widely used for minimizing a continuous
function f of several block variables. At each iteration of this method, a single block of …

We describe an asynchronous parallel stochastic proximal coordinate descent algorithm for
minimizing a composite objective function, which consists of a smooth convex function …

We propose a decomposition framework for the parallel optimization of the sum of a
differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex …

The stochastic gradient (SG) method can quickly solve a problem with a large number of
components in the objective, or a stochastic optimization problem, to a moderate accuracy …