4 papers
A randomized operator splitting scheme inspired by stochastic optimization methods
Monika Eisenmann, Tony Stillfjord
In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale optimization problems. The combination…
SRKCD: a stabilized Runge-Kutta method for stochastic optimization
Tony Stillfjord, Måns Williamson
We introduce a family of stochastic optimization methods based on the Runge-Kutta-Chebyshev (RKC) schemes. The RKC methods are explicit methods originally designed for solving stif…
Sub-linear convergence of a tamed stochastic gradient descent method in Hilbert space
Monika Eisenmann, Tony Stillfjord
In this paper, we introduce the tamed stochastic gradient descent method (TSGD) for optimization problems. Inspired by the tamed Euler scheme, which is a commonly used method withi…
Sub-linear convergence of a stochastic proximal iteration method in Hilbert space
Monika Eisenmann, Tony Stillfjord, Måns Williamson
We consider a stochastic version of the proximal point algorithm for optimization problems posed on a Hilbert space. A typical application of this is supervised learning. While the…