With the growth of data and necessity for distributed optimization methods, solvers that work
well on a single machine must be re-designed to leverage distributed computation. Recent …

In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method for
solving loss minimization problems with big data. We initially partition the coordinates …

This paper considers decentralized consensus optimization problems where different
summands of a global objective function are available at nodes of a network that can …

We study the problem of minimizing the sum of a smooth convex function and a convex
block-separable regularizer and propose a new randomized coordinate descent method …

We study the problem of minimizing the average of a large number of smooth convex
functions penalized with a strongly convex regularizer. We propose and analyze a novel …

We propose a new algorithm for minimizing regularized empirical loss: Stochastic Dual
Newton Ascent (SDNA). Our method is dual in nature: in each iteration we update a random …

We propose and analyze a new parallel coordinate descent method—NSync—in which at
each iteration a random subset of coordinates is updated, in parallel, allowing for the …

This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent
(SDCA) for solving the regularized empirical risk minimization problems. Our modification …

Industrial scheduling problems (ISPs), especially industrial production workshop scheduling
problems (IPWSPs) in various sectors like manufacturing, and power require allocating …

The design and complexity analysis of randomized coordinate descent methods, and in
particular of variants which update a random subset (sampling) of coordinates in each …