Vol. 4, No. 1, 2009

Download this article
Download this article For screen
For printing
Recent Issues
Volume 18, Issue 1
Volume 17, Issue 1
Volume 16, Issue 2
Volume 16, Issue 1
Volume 15, Issue 2
Volume 15, Issue 1
Volume 14, Issue 2
Volume 14, Issue 1
Volume 13, Issue 2
Volume 13, Issue 1
Volume 12, Issue 1
Volume 11, Issue 2
Volume 11, Issue 1
Volume 10, Issue 2
Volume 10, Issue 1
Volume 9, Issue 2
Volume 9, Issue 1
Volume 8, Issue 1
Volume 7, Issue 2
Volume 7, Issue 1
Volume 6, Issue 1
Volume 5, Issue 2
Volume 5, Issue 1
Volume 4, Issue 1
Volume 3, Issue 1
Volume 2, Issue 1
Volume 1, Issue 1
The Journal
About the Journal
Editorial Board
Submission Guidelines
Submission Form
Policies for Authors
Ethics Statement
ISSN: 2157-5452 (e-only)
ISSN: 1559-3940 (print)
Author Index
To Appear
Other MSP Journals
Parallel overlapping domain decomposition methods for coupled inverse elliptic problems

Xiao-Chuan Cai, Si Liu and Jun Zou

Vol. 4 (2009), No. 1, 1–26

We study an overlapping domain decomposition method for solving the coupled nonlinear system of equations arising from the discretization of inverse elliptic problems. Most algorithms for solving inverse problems take advantage of the fact that the optimality system has a natural splitting into three components: the state equation for the constraints, the adjoint equation for the Lagrange multipliers, and the equation for the parameter to be identified. Such algorithms often involve interiterations between the three separate solvers, and the intercomponent iteration is sequential. Several fully coupled or so-called one-shot approaches exist, and the main challenges in these approaches are that the system has stronger nonlinearity, and the corresponding Jacobian system is more ill-conditioned, in addition to being three times larger. Here we investigate a class of overlapping Newton–Krylov–Schwarz algorithms for solving such coupled systems, obtained with a pointwise ordering of the variables, and show numerically that, with a reasonably large overlap, the algorithm is capable of finding the solution even with noise and discontinuous coefficients. More importantly, we show that this approach is fully parallel and scalable with respect to the size of the problems.

inverse problems, domain decomposition, parallel computing, partial differential equations constrained optimiztion, inexact Newton
Mathematical Subject Classification 2000
Primary: 65N21, 65N55, 65Y05
Received: 24 March 2008
Accepted: 15 March 2009
Published: 11 June 2009
Xiao-Chuan Cai
Department of Computer Science
University of Colorado at Boulder
Boulder, CO 80309
United States
Si Liu
Department of Applied Mathematics
University of Colorado at Boulder
Boulder, CO 80309
United States
Jun Zou
Department of Mathematics
The Chinese University of Hong Kong
Shatin, N. T., Hong Kong