3k citations · 9k across the 154 of their papers we have counts for
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Accelerated, Parallel and Proximal Coordinate Descent
Olivier Fercoq, Peter Richtárik
We propose a new stochastic coordinate descent method for minimizing the sum of convex functions each of which depends on a small number of coordinates only. Our method (APPROX) is…
Semi-Stochastic Gradient Descent Methods
Jakub Konečný, Peter Richtárik
In this paper we study the problem of minimizing the average of a large number () of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descen…
TOP-SPIN: TOPic discovery via Sparse Principal component INterference
Martin Takáč, Selin Damla Ahipaşaoğlu, Ngai-Man Cheung +1
We propose a novel topic discovery algorithm for unlabeled images based on the bag-of-words (BoW) framework. We first extract a dictionary of visual words and subsequently for each…
On Optimal Probabilities in Stochastic Coordinate Descent Methods
Peter Richtárik, Martin Takáč
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 subse…
Distributed Coordinate Descent Method for Learning with Big Data
Peter Richtárik, Martin Takáč
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 (features)…
Smooth minimization of nonsmooth functions with parallel coordinate descent methods
Olivier Fercoq, Peter Richtárik
We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions. The problem class inclu…