Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Geiersbach, Caroline"'
Autor:
Dvurechensky, Pavel, Geiersbach, Caroline, Hintermüller, Michael, Kannan, Aswin, Kater, Stefan, Zöttl, Gregor
We present a novel model of a coupled hydrogen and electricity market on the intraday time scale, where hydrogen gas is used as a storage device for the electric grid. Electricity is produced by renewable energy sources or by extracting hydrogen from
Externí odkaz:
http://arxiv.org/abs/2410.20534
The present article is dedicated to proving convergence of the stochastic gradient method in case of random shape optimization problems. To that end, we consider Bernoulli's exterior free boundary problem with a random interior boundary. We recast th
Externí odkaz:
http://arxiv.org/abs/2408.05021
We investigate a complex system involving multiple shapes to be optimized in a domain, taking into account geometric constraints on the shapes and uncertainty appearing in the physics. We connect the differential geometry of product shape manifolds w
Externí odkaz:
http://arxiv.org/abs/2308.07742
Autor:
Geiersbach, Caroline, Henrion, René
In this paper, we discuss optimality conditions for optimization problems involving random state constraints, which are modeled in probabilistic or almost sure form. While the latter can be understood as the limiting case of the former, the derivatio
Externí odkaz:
http://arxiv.org/abs/2306.03965
In this paper, we present a stochastic augmented Lagrangian approach on (possibly infinite-dimensional) Riemannian manifolds to solve stochastic optimization problems with a finite number of deterministic constraints.We investigate the convergence of
Externí odkaz:
http://arxiv.org/abs/2303.17404
We consider a risk-averse optimal control problem governed by an elliptic variational inequality (VI) subject to random inputs. By deriving KKT-type optimality conditions for a penalised and smoothed problem and studying convergence of the stationary
Externí odkaz:
http://arxiv.org/abs/2210.03425
Autor:
Geiersbach, Caroline, Scarinci, Teresa
The study of optimal control problems under uncertainty plays an important role in scientific numerical simulations. This class of optimization problems is strongly utilized in engineering, biology and finance. In this paper, a stochastic gradient me
Externí odkaz:
http://arxiv.org/abs/2108.11782
We analyze a potentially risk-averse convex stochastic optimization problem, where the control is deterministic and the state is a Banach-valued essentially bounded random variable. We obtain strong forms of necessary and sufficient optimality condit
Externí odkaz:
http://arxiv.org/abs/2108.01391
Shape optimization models with one or more shapes are considered in this chapter. Of particular interest for applications are problems in which where a so-called shape functional is constrained by a partial differential equation (PDE) describing the
Externí odkaz:
http://arxiv.org/abs/2107.07744
Publikováno v:
SIAM J. Optim. Vol. 31, 2021
We analyze a convex stochastic optimization problem where the state is assumed to belong to the Bochner space of essentially bounded random variables with images in a reflexive and separable Banach space. For this problem, we obtain optimality condit
Externí odkaz:
http://arxiv.org/abs/2009.04168