A duality-based approach to elliptic control problems in non-reflexive Banach spaces
By Christian Clason, Karl Kunisch. 2011
125 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/2b810b8071d8895224c761f30372be77f6de1303
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Hanna K. Pikkarainen
- Kristian Bredies
- Eduardo Casas
- Karl Kunisch
- Emmanuel Trélat
- Massimo Fornasier
- Benedetto Piccoli
- Marco Caponigro
In Computational Theory and Mathematics
- Hanna K. Pikkarainen
- Kristian Bredies
- Eduardo Casas
- Emmanuel Trélat
- Benedetto Piccoli
- Massimo Fornasier
- Marco Caponigro
- Karl Kunisch
Citations in the corpus
- Sparse initial data identification for parabolic PDE and its finite element approximations (2015, unknown): “Sparse optimal control problems in measure spaces are analyzed in [11, 26, 7] for elliptic and in [5, 21] for parabolic equations, where in both last papers also numerical analysis of spacetime discretizations is performed, see also [15, 22] for related works on numerical analysis of pointwise contr”
- Parabolic control problems in space-time measure spaces (2016, background): “The papers [5,8] may have been the first ones to address measure valued control problems with the goal of sparsity for linear elliptic control systems.”
- Inverse problems in spaces of measures (2013, methodology): “In [11], elliptic partial differential equations are controlled with a Radon measure penalty.”
- Sparse stabilization and optimal control of the Cucker-Smale model (2013, methodology): “…optimal control with partial differential equation constraints it became rather popular to use L 1 - minimization to enforce sparsity of controls [9, 13, 14, 31, 49, 58, 65], for instance in the optimal placement of actuators or sensors (see also the recent works [53, 52, 51] where the sparsity is…”