Block-Symmetric and Block-Lower-Triangular Preconditioners for PDE-Constrained Optimization Problems

Block-Symmetric and Block-Lower-Triangular Preconditioners for PDE-Constrained Optimization Problems

Year:    2013

Journal of Computational Mathematics, Vol. 31 (2013), Iss. 4 : pp. 370–381

Abstract

Optimization problems with partial differential equations as constraints arise widely in many areas of science and engineering, in particular in problems of the design. The solution of such class of PDE-constrained optimization problems is usually a major computational task. Because of the complexion for directly seeking the solution of PDE-constrained optimization problem, we transform it into a system of linear equations of the saddle-point form by using the Galerkin finite-element discretization. For the discretized linear system, in this paper we construct a block-symmetric and a block-lower-triangular preconditioner, for solving the PDE-constrained optimization problem. Both preconditioners exploit the structure of the coefficient matrix. The explicit expressions for the eigenvalues and eigenvectors of the corresponding preconditioned matrices are derived. Numerical implementations show that these block preconditioners can lead to satisfactory experimental results for the preconditioned GMRES methods when the regularization parameter is suitably small.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1301-m4234

Journal of Computational Mathematics, Vol. 31 (2013), Iss. 4 : pp. 370–381

Published online:    2013-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    12

Keywords:    Saddle-point matrix Preconditioning PDE-constrained optimization Eigenvalue and eigenvector Regularization parameter.

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