438 citations · 1k across the 57 of their papers we have counts for
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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…
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
Hamid Reza Feyzmahdavian, Mikael Johansson
We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms. The results are simple to apply…
Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
Xiaoyu Wang, Mikael Johansson
Many popular learning-rate schedules for deep neural networks combine a decaying trend with local perturbations that attempt to escape saddle points and bad local minima. We derive…
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…