Showing math.OCShow all
2 papers · 1 filter
math.OC2026
Bregman Stochastic Proximal Point Algorithm with Variance Reduction
Cheik Traoré, Peter Ochs
Stochastic algorithms, especially stochastic gradient descent (SGD), have proven to be the go-to methods in data science and machine learning. In recent years, the stochastic proxi…
math.OC2024
Automatic Differentiation of Optimization Algorithms with Time-Varying Updates
Sheheryar Mehmood, Peter Ochs
Numerous Optimization Algorithms have a time-varying update rule thanks to, for instance, a changing step size, momentum parameter or, Hessian approximation. In this paper, we appl…