no code implementations • 2 Dec 2023 • Junwen Qiu, Xiao Li, Andre Milzarek
In this work, we design a new normal map-based proximal random reshuffling (norm-PRR) method for nonsmooth nonconvex finite-sum problems.
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