4 citations · 5 across the 3 of their papers we have counts for
6 papers
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…
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…
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization
Vien V. Mai, Mikael Johansson
Stochastic gradient methods with momentum are widely used in applications and at the core of optimization subroutines in many popular machine learning libraries. However, their sam…
Anderson Acceleration of Proximal Gradient Methods
Vien V. Mai, Mikael Johansson
Anderson acceleration is a well-established and simple technique for speeding up fixed-point computations with countless applications. Previous studies of Anderson acceleration in…
Noisy Accelerated Power Method for Eigenproblems with Applications
Vien V. Mai, Mikael Johansson
This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex…
Curvature-Exploiting Acceleration of Elastic Net Computations
Vien V. Mai, Mikael Johansson
This paper introduces an efficient second-order method for solving the elastic net problem. Its key innovation is a computationally efficient technique for injecting curvature info…