8 citations · 25 across the 11 of their papers we have counts for
15 papers · 1 filter
Optimal convergence rates of totally asynchronous optimization
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information dela…
A Fast and Accurate Splitting Method for Optimal Transport: Analysis and Implementation
Vien V. Mai, Jacob Lindbäck, Mikael Johansson
We develop a fast and reliable method for solving large-scale optimal transport (OT) problems at an unprecedented combination of speed and accuracy. Built on the celebrated Douglas…
A New Family of Feasible Methods for Distributed Resource Allocation
Xuyang Wu, Sindri Magnusson, Mikael Johansson
Distributed resource allocation is a central task in network systems such as smart grids, water distribution networks, and urban transportation systems. When solving such problems…
On the Convergence of Step Decay Step-Size for Stochastic Optimization
Xiaoyu Wang, Sindri Magnússon, Mikael Johansson
The convergence of stochastic gradient descent is highly dependent on the step-size, especially on non-convex problems such as neural network training. Step decay step-size schedul…
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
Vien V. Mai, Mikael Johansson
Stochastic gradient algorithms are often unstable when applied to functions that do not have Lipschitz-continuous and/or bounded gradients. Gradient clipping is a simple and effect…
A flexible framework for communication-efficient machine learning: from HPC to IoT
Sarit Khirirat, Sindri Magnússon, Arda Aytekin +1
With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes. Early work on gradient compression focused on the…