260 citations · 1.8k across the 63 of their papers we have counts for
8 papers · 1 filter
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning
Chenxin Ma, Martin Jaggi, Frank E. Curtis +2
Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Conf…
A Coordinate-Descent Algorithm for Tracking Solutions in Time-Varying Optimal Power Flows
Jie Liu, Jakub Marecek, Andrea Simonetto +1
Consider a polynomial optimisation problem, whose instances vary continuously over time. We propose to use a coordinate-descent algorithm for solving such time-varying optimisation…
Fast and Safe: Accelerated gradient methods with optimality certificates and underestimate sequences
Majid Jahani, Naga Venkata C. Gudapati, Chenxin Ma +2
In this work we introduce the concept of an Underestimate Sequence (UES), which is motivated by Nesterov's estimate sequence. Our definition of a UES utilizes three sequences, one…
Stock-out Prediction in Multi-echelon Networks
Afshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč
In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and in many other time periods. For example, a supply disrup…
A Robust Multi-Batch L-BFGS Method for Machine Learning
Albert S. Berahas, Martin Takáč
This paper describes an implementation of the L-BFGS method designed to deal with two adversarial situations. The first occurs in distributed computing environments where some of t…
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Peter Richtárik, Martin Takáč
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive…
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving emp…
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. D…