English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The study of optimal control problems under uncertainty plays an importantrole in scientific numerical simulations. This class of optimization problemsis strongly utilized in engineering, biology and finance. In this paper, astochastic gradient method is proposed for the numerical resolution of anonconvex stochastic optimization problem on a Hilbert space. We show that,under suitable assumptions, strong or weak accumulation points of the iteratesproduced by the method converge almost surely to stationary points of theoriginal optimization problem. Measurability and convergence rates of astationarity measure are handled, filling a gap for applications to nonconvexinfinite dimensional stochastic optimization problems. The method isdemonstrated on an optimal control problem constrained by a class of ellipticsemilinear partial differential equations (PDEs) under uncertainty.

A stochastic gradient method for a class of nonlinear PDE-constrained optimal control problems under uncertainty