2007
DOI: 10.1007/s10589-007-9150-9
|View full text |Cite
|
Sign up to set email alerts
|

Elliptic optimal control problems with L 1-control cost and applications for the placement of control devices

Abstract: Elliptic optimal control problems with L 1 -control cost are analyzed. Due to the nonsmooth objective functional the optimal controls are identically zero on large parts of the control domain. For applications, in which one cannot put control devices (or actuators) all over the control domain, this provides information about where it is most efficient to put them. We analyze structural properties of L 1 -control cost solutions. For solving the non-differentiable optimal control problem we propose a semismooth … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

2
249
0

Year Published

2010
2010
2019
2019

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 216 publications
(251 citation statements)
references
References 33 publications
2
249
0
Order By: Relevance
“…Therefore, it is also called sparse control or sparse optimal control in the literature, see Refs. [34][35][36][37]. In some sense, we can interpret the areas with non-vanishing sparse optimal control signals as the most sensitive areas of the RD patterns with respect to the desired control goal.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, it is also called sparse control or sparse optimal control in the literature, see Refs. [34][35][36][37]. In some sense, we can interpret the areas with non-vanishing sparse optimal control signals as the most sensitive areas of the RD patterns with respect to the desired control goal.…”
Section: Introductionmentioning
confidence: 99%
“…Problem (P * L 1 ,L 2 ) therefore can be seen as a regularization of (P * M ) by introducing a new variable q = −p and treating this equality constraint by penalization (cf. also [11]). In Section 3 we will directly regularize the dual problem (P…”
Section: Regularization Of (P M )mentioning
confidence: 87%
“…For example, it is known that L 1 (Ω)-type costs promote sparsity, whereas BV(Ω)-type penalties favor piecewise constant functions (cf. [10,11], respectively). Note that for X = L 1 (Ω), problem (P) is not well-posed: It need not have a minimizer in L 1 (Ω), since the conditions of the Dunford-Pettis theorem are not satisfied (boundedness in L 1 (Ω) is not a sufficient condition for the existence of a weakly converging subsequence).…”
Section: Introductionmentioning
confidence: 97%
“…Optimal control problems with partial differential equations (PDEs) and sparsity promoting terms were first considered in Stadler [2009], who studied optimality conditions, parameter dependence and a semismooth Newton method in the convex case governed by a linear elliptic PDE. A priori and a posteriori error estimates for this case were provided in Wachsmuth and Wachsmuth [2011].…”
Section: Introductionmentioning
confidence: 99%