Volume 39, Issue 3
Error Estimates for Sparse Optimal Control Problems by Piecewise Linear Finite Element Approximation

J. Comp. Math., 39 (2021), pp. 471-492.

Published online: 2021-04

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• Abstract

Optimization problems with $L^1$-control cost functional subject to an elliptic partial differential equation (PDE) are considered. However, different from the finite dimensional $l^1$-regularization optimization, the resulting discretized $L^1$-norm does not have a decoupled form when the standard piecewise linear finite element is employed to discretize the continuous problem. A common approach to overcome this difficulty is employing a nodal quadrature formula to approximately discretize the $L^1$-norm. In this paper, a new discretized scheme for the $L^1$-norm is presented. Compared to the new discretized scheme for $L^1$-norm with the nodal quadrature formula, the advantages of our new discretized scheme can be demonstrated in terms of the order of approximation. Moreover, finite element error estimates results for the primal problem with the new discretized scheme for the $L^1$-norm are provided, which confirms that this approximation scheme will not change the order of error estimates. To solve the new discretized problem, a symmetric Gauss-Seidel based majorized accelerated block coordinate descent (sGS-mABCD) method is introduced to solve it via its dual. The proposed sGS-mABCD algorithm is illustrated at two numerical examples. Numerical results not only confirm the finite element error estimates, but also show that our proposed algorithm is efficient.

• Keywords

Finite element method, ABCD method, Approximate discretization, Error estimates.

49N05, 65N30, 49M25, 68W15.

chenbo@u.nus.edu (Bo Chen)

yubo@dlut.edu.cn (Bo Yu)

• BibTex
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@Article{JCM-39-471, author = {Xiaoliang Song , and Chen , Bo and Yu , Bo}, title = {Error Estimates for Sparse Optimal Control Problems by Piecewise Linear Finite Element Approximation}, journal = {Journal of Computational Mathematics}, year = {2021}, volume = {39}, number = {3}, pages = {471--492}, abstract = {

Optimization problems with $L^1$-control cost functional subject to an elliptic partial differential equation (PDE) are considered. However, different from the finite dimensional $l^1$-regularization optimization, the resulting discretized $L^1$-norm does not have a decoupled form when the standard piecewise linear finite element is employed to discretize the continuous problem. A common approach to overcome this difficulty is employing a nodal quadrature formula to approximately discretize the $L^1$-norm. In this paper, a new discretized scheme for the $L^1$-norm is presented. Compared to the new discretized scheme for $L^1$-norm with the nodal quadrature formula, the advantages of our new discretized scheme can be demonstrated in terms of the order of approximation. Moreover, finite element error estimates results for the primal problem with the new discretized scheme for the $L^1$-norm are provided, which confirms that this approximation scheme will not change the order of error estimates. To solve the new discretized problem, a symmetric Gauss-Seidel based majorized accelerated block coordinate descent (sGS-mABCD) method is introduced to solve it via its dual. The proposed sGS-mABCD algorithm is illustrated at two numerical examples. Numerical results not only confirm the finite element error estimates, but also show that our proposed algorithm is efficient.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.2003-m2017-0213}, url = {http://global-sci.org/intro/article_detail/jcm/18748.html} }
TY - JOUR T1 - Error Estimates for Sparse Optimal Control Problems by Piecewise Linear Finite Element Approximation AU - Xiaoliang Song , AU - Chen , Bo AU - Yu , Bo JO - Journal of Computational Mathematics VL - 3 SP - 471 EP - 492 PY - 2021 DA - 2021/04 SN - 39 DO - http://doi.org/10.4208/jcm.2003-m2017-0213 UR - https://global-sci.org/intro/article_detail/jcm/18748.html KW - Finite element method, ABCD method, Approximate discretization, Error estimates. AB -

Optimization problems with $L^1$-control cost functional subject to an elliptic partial differential equation (PDE) are considered. However, different from the finite dimensional $l^1$-regularization optimization, the resulting discretized $L^1$-norm does not have a decoupled form when the standard piecewise linear finite element is employed to discretize the continuous problem. A common approach to overcome this difficulty is employing a nodal quadrature formula to approximately discretize the $L^1$-norm. In this paper, a new discretized scheme for the $L^1$-norm is presented. Compared to the new discretized scheme for $L^1$-norm with the nodal quadrature formula, the advantages of our new discretized scheme can be demonstrated in terms of the order of approximation. Moreover, finite element error estimates results for the primal problem with the new discretized scheme for the $L^1$-norm are provided, which confirms that this approximation scheme will not change the order of error estimates. To solve the new discretized problem, a symmetric Gauss-Seidel based majorized accelerated block coordinate descent (sGS-mABCD) method is introduced to solve it via its dual. The proposed sGS-mABCD algorithm is illustrated at two numerical examples. Numerical results not only confirm the finite element error estimates, but also show that our proposed algorithm is efficient.

Xiaoliang Song, Bo Chen & Bo Yu. (2021). Error Estimates for Sparse Optimal Control Problems by Piecewise Linear Finite Element Approximation. Journal of Computational Mathematics. 39 (3). 471-492. doi:10.4208/jcm.2003-m2017-0213
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