32 citations · 63 across the 15 of their papers we have counts for
3 papers · 1 filter
SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization
Derek Driggs, Junqi Tang, Jingwei Liang +2
We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems. Large-scale i…
Accelerating Variance-Reduced Stochastic Gradient Methods
Derek Driggs, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb
Variance reduction is a crucial tool for improving the slow convergence of stochastic gradient descent. Only a few variance-reduced methods, however, have yet been shown to directl…
A geometric integration approach to nonsmooth, nonconvex optimisation
Erlend S. Riis, Matthias J. Ehrhardt, G. R. W. Quispel +1
The optimisation of nonsmooth, nonconvex functions without access to gradients is a particularly challenging problem that is frequently encountered, for example in model parameter…