TY - JOUR
T1 - Block-Symmetric and Block-Lower-Triangular Preconditioners for PDE-Constrained Optimization Problems
AU - Guofeng Zhang & Zhong Zheng
JO - Journal of Computational Mathematics
VL - 4
SP - 370
EP - 381
PY - 2013
DA - 2013/08
SN - 31
DO - http://doi.org/10.4208/jcm.1301-m4234
UR - https://global-sci.org/intro/article_detail/jcm/9741.html
KW - Saddle-point matrix, Preconditioning, PDE-constrained optimization, Eigenvalue and eigenvector, Regularization parameter.
AB - <p style="text-align: justify;">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.</p>