Search Results for author: Andre Milzarek

Found 9 papers, 2 papers with code

A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization

no code implementations2 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.

Stochastic Optimization

Convergence of a Normal Map-based Prox-SGD Method under the KL Inequality

no code implementations10 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.

A Unified Convergence Theorem for Stochastic Optimization Methods

no code implementations8 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.

Stochastic Optimization

A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction

1 code implementation1 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.

Distributed Random Reshuffling over Networks

no code implementations31 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.

Distributed Optimization

Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality

no code implementations10 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.

A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization

no code implementations21 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.

A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization

no code implementations9 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.

Binary Classification Stochastic Optimization

Adaptive Regularized Newton Method for Riemannian Optimization

2 code implementations7 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

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