260 citations · 1.8k across the 63 of their papers we have counts for
9 papers · 1 filter
Distributed Optimization with Arbitrary Local Solvers
Chenxin Ma, Jakub Konečný, Martin Jaggi +4
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. Rece…
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?
Chenxin Ma, Martin Takáč
In this paper we study the effect of the way that the data is partitioned in distributed optimization. The original DiSCO algorithm [Communication-Efficient Distributed Optimizatio…
Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization
Xi He, Martin Takáč
In this paper we develop dual free mini-batch SDCA with adaptive probabilities for regularized empirical risk minimization. This work is motivated by recent work of Shai Shalev-Shw…
Hybrid Methods in Solving Alternating-Current Optimal Power Flows
Alan C. Liddell, Jie Liu, Jakub Marecek +1
Many steady-state problems in power systems, including rectangular power-voltage formulations of optimal power flows in the alternating-current model (ACOPF), can be cast as polyno…
Distributed Mini-Batch SDCA
Martin Takáč, Peter Richtárik, Nathan Srebro
We present an improved analysis of mini-batched stochastic dual coordinate ascent for regularized empirical loss minimization (i.e. SVM and SVM-type objectives). Our analysis allow…
A Low-Rank Coordinate-Descent Algorithm for Semidefinite Programming Relaxations of Optimal Power Flow
Jakub Marecek, Martin Takac
The alternating-current optimal power flow (ACOPF) is one of the best known non-convex non-linear optimisation problems. We present a novel re-formulation of ACOPF, which is based…
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konečný, Jie Liu, Peter Richtárik +1
We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We co…
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu, Peter Richtárik, Martin Takáč +1
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 subse…
Adding vs. Averaging in Distributed Primal-Dual Optimization
Chenxin Ma, Virginia Smith, Martin Jaggi +3
Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accu…