Stability Analysis of the Inverse Problem of Parameter Identification in Mixed Variational Problems

M. Cho (), A. A. Khan (), T. Malysheva (), M. Sama () and L. White ()
Additional contact information
M. Cho: Rochester Institute of Technology
A. A. Khan: Rochester Institute of Technology
T. Malysheva: University of Wisconsin-Green Bay
M. Sama: Universidad Nacional de Educación a Distancia
L. White: University of Oklahoma

A chapter in Applications of Nonlinear Analysis, 2018, pp 61-100 from Springer

Abstract: Abstract Numerous applications lead to inverse problems of parameter identification in mixed variational problems. These inverse problems are commonly studied as optimization problems, and there are a variety of optimization formulations. The known formulations include an output least-squares (OLS), an energy OLS (EOLS), and a modified OLS (MOLS). This work conducts a detailed study of various stability aspects of the inverse problem under data perturbation and gives new stability estimates for general inverse problems using the OLS, EOLS, and MOLS formulations. We present applications of our theoretical results.

Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-89815-5_4

Ordering information: This item can be ordered from
http://www.springer.com/9783319898155

DOI: 10.1007/978-3-319-89815-5_4

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().