260 citations · 1.8k across the 63 of their papers we have counts for
5 papers · 1 filter
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konečný, Jie Liu, Peter Richtárik +1
We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a stro…
Communication-Efficient Distributed Dual Coordinate Ascent
Martin Jaggi, Virginia Smith, Martin Takáč +4
Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper, we propose a commun…
Matrix Completion under Interval Uncertainty
Jakub Marecek, Peter Richtarik, Martin Takac
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-d…
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…
Fast Distributed Coordinate Descent for Non-Strongly Convex Losses
Olivier Fercoq, Zheng Qu, Peter Richtárik +1
We propose an efficient distributed randomized coordinate descent method for minimizing regularized non-strongly convex loss functions. The method attains the optimal co…