no code implementations • 2 Dec 2023 • Xiao Li, Andre Milzarek, Junwen Qiu
While the convergence behavior and advantageous acceleration effects of random reshuffling methods are fairly well understood in the smooth setting, much less seems to be known in the nonsmooth case and only few proximal-type random reshuffling approaches with provable guarantees exist.
no code implementations • 10 May 2023 • Andre Milzarek, Junwen Qiu
In this paper, we present a novel stochastic normal map-based algorithm ($\mathsf{norM}\text{-}\mathsf{SGD}$) for nonconvex composite-type optimization problems and discuss its convergence properties.
no code implementations • 8 Jun 2022 • Xiao Li, Andre Milzarek
In this work, we provide a fundamental unified convergence theorem used for deriving expected and almost sure convergence results for a series of stochastic optimization methods.
1 code implementation • 1 Apr 2022 • Andre Milzarek, Fabian Schaipp, Michael Ulbrich
We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems.
no code implementations • 31 Dec 2021 • Kun Huang, Xiao Li, Andre Milzarek, Shi Pu, Junwen Qiu
We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions.
no code implementations • 10 Oct 2021 • Xiao Li, Andre Milzarek, Junwen Qiu
We conduct a novel convergence analysis for the non-descent RR method with diminishing step sizes based on the KL inequality, which generalizes the standard KL framework.
no code implementations • 21 Oct 2019 • Ming-Han Yang, Andre Milzarek, Zaiwen Wen, Tong Zhang
In this paper, a novel stochastic extra-step quasi-Newton method is developed to solve a class of nonsmooth nonconvex composite optimization problems.
no code implementations • 9 Mar 2018 • Andre Milzarek, Xiantao Xiao, Shicong Cen, Zaiwen Wen, Michael Ulbrich
In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the objective function.
2 code implementations • 7 Aug 2017 • Jiang Hu, Andre Milzarek, Zaiwen Wen, Yaxiang Yuan
Optimization on Riemannian manifolds widely arises in eigenvalue computation, density functional theory, Bose-Einstein condensates, low rank nearest correlation, image registration, and signal processing, etc.
Optimization and Control