260 citations · 1.8k across the 63 of their papers we have counts for
4 papers · 2 filters
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…
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…