Stochastic Subspace Correction in Hilbert Space

被引：0

作者：

Michael Griebel

Peter Oswald

机构：

[1] Universität Bonn,Institute for Numerical Simulation

[2] Schloss Birlinghoven,Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)

来源：

Constructive Approximation | 2018年 / 48卷

关键词：

Infinite space splitting; Subspace correction; Multiplicative Schwarz; Block coordinate descent; Greedy; Randomized; Convergence rates; Online learning; 65F10; 65N22; 49M27;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We consider an incremental approximation method for solving variational problems in infinite-dimensional separable Hilbert spaces, where in each step a randomly and independently selected subproblem from an infinite collection of subproblems is solved. We show that convergence rates for the expectation of the squared error can be guaranteed under weaker conditions than previously established in Griebel and Oswald (Constr Approx 44(1):121–139, 2016).

引用

页码：501 / 521

页数：20

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