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Semi-Stochastic Coordinate Descent
Jakub Konečný, Zheng Qu, Peter Richtárik
We propose a novel stochastic gradient method---semi-stochastic coordinate descent (S2CD)---for the problem of minimizing a strongly convex function represented as the average of a…
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation
Zheng Qu, Peter Richtárik
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 iteration,…
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity
Zheng Qu, Peter Richtárik
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, which we cal…
Randomized Dual Coordinate Ascent with Arbitrary Sampling
Zheng Qu, Peter Richtárik, Tong Zhang
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 primal-dual…
Simple Complexity Analysis of Simplified Direct Search
Jakub Konečný, Peter Richtárik
We consider the problem of unconstrained minimization of a smooth function in the derivative-free setting using. In particular, we propose and study a simplified variant of the dir…
Distributed Block Coordinate Descent for Minimizing Partially Separable Functions
Jakub Marecek, Peter Richtarik, Martin Takac
In this work we propose a distributed randomized block coordinate descent method for minimizing a convex function with a huge number of variables/coordinates. We analyze its comple…