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20172022
most citedOptimal control of an evolution equation with non-smooth dissipation

2 citations · 2 across the 10 of their papers we have counts for

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15 papers · 1 filter

math.OC2021

A penalty scheme to solve constrained non-convex optimization problems in

Carolin Natemeyer, Daniel Wachsmuth

We investigate non-convex optimization problems in with two-sided pointwise inequality constraints. We propose a regularization and penalization method to numerically solve…

math.OC2020

A proximal gradient method for control problems with nonsmooth and nonconvex control cost

Carolin Natemeyer, Daniel Wachsmuth

We investigate the convergence of an application of a proximal gradient method to control problems with nonsmooth and nonconvex control cost. Here, we focus on control cost functio…

math.OC2020

Full Stability for Variational Nash Equilibriums of Parametric Optimal Control Problems of PDEs

Nguyen Thanh Qui, Daniel Wachsmuth

This paper investigates full stability properties for \emph{variational Nash equilibriums} of a system of parametric nonconvex optimal control problems governed by semilinear ellip…

math.OC2020

Analysis of Optimal Control Problems with an Term in the Cost Functional

Eduardo Casas, Daniel Wachsmuth

In this paper, we investigate optimal control problems subject to a semilinear elliptic partial differential equations. The cost functional contains a term that measures the size o…

math.OC2018

Optimal control of ODEs with state suprema

Tobias Geiger, Daniel Wachsmuth, Gerd Wachsmuth

We consider the optimal control of a differential equation that involves the suprema of the state over some part of the history. In many applications, this non-smooth functional de…

math.OC2018

Subgradients of Marginal Functions in Parametric Control Problems of Partial Differential Equations

Nguyen Thanh Qui, Daniel Wachsmuth

The paper studies generalized differentiability properties of the marginal function of parametric optimal control problems of semilinear elliptic partial differential equations. We…