2 citations · 3 across the 13 of their papers we have counts for
4 papers · 2 filters
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods
Junwen Qiu, Bohao Ma, Xiao Li +1
We propose a novel analysis framework for non-descent-type optimization methodologies in nonconvex scenarios based on the Kurdyka-Lojasiewicz property. Our framework allows coverin…
A Generalized Version of Chung's Lemma and its Applications
Li Jiang, Xiao Li, Andre Milzarek +1
Chung's Lemma is a classical tool for establishing asymptotic convergence rates of (stochastic) optimization methods under strong convexity-type assumptions and appropriate polynom…
Convergence of SGD with momentum in the nonconvex case: A time window-based analysis
Junwen Qiu, Bohao Ma, Andre Milzarek
The stochastic gradient descent method with momentum (SGDM) is a common approach for solving large-scale and stochastic optimization problems. Despite its popularity, the convergen…
Random Reshuffling with Momentum: Complexity Bounds and Last-iterate Convergence
Junwen Qiu, Bohao Ma, Andre Milzarek
Random reshuffling with momentum (RRM) corresponds to the SGD optimizer with the 'momentum' option enabled, as found in many machine learning libraries such as PyTorch and TensorFl…