260 citations · 1.8k across the 63 of their papers we have counts for
7 papers · 1 filter
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption
Jie Liu, Martin Takac
We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gra…
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Virginia Smith, Simone Forte, Chenxin Ma +3
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed…
Applying Deep Learning to the Newsvendor Problem
Afshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč
The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the probability distribution of the demand is…
Distributed Hessian-Free Optimization for Deep Neural Network
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy +1
Training deep neural network is a high dimensional and a highly non-convex optimization problem. Stochastic gradient descent (SGD) algorithm and it's variations are the current sta…
A Multi-Batch L-BFGS Method for Machine Learning
Albert S. Berahas, Jorge Nocedal, Martin Takáč
The question of how to parallelize the stochastic gradient descent (SGD) method has received much attention in the literature. In this paper, we focus instead on batch methods that…
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing
Chenxin Ma, Martin Takáč
In this paper we study inexact dumped Newton method implemented in a distributed environment. We start with an original DiSCO algorithm [Communication-Efficient Distributed Optimiz…
Primal-Dual Rates and Certificates
Celestine Dünner, Simone Forte, Martin Takáč +1
We propose an algorithm-independent framework to equip existing optimization methods with primal-dual certificates. Such certificates and corresponding rate of convergence guarante…