A FE-ADMM algorithm for Lavrentiev-regularized state-constrained elliptic control problem
ESAIM: Control, Optimisation and Calculus of Variations, Tome 25 (2019), article no. 5.

In this paper, Elliptic control problems with pointwise box constraints on the state is considered, where the corresponding Lagrange multipliers in general only represent regular Borel measure functions. To tackle this difficulty, the Lavrentiev regularization is employed to deal with the state constraints. To numerically discretize the resulted problem, full piecewise linear finite element discretization is employed. Estimation of the error produced by regularization and discretization is done. The error order of full discretization is not inferior to that of variational discretization because of the Lavrentiev-regularization. Taking the discretization error into account, algorithms of high precision do not make much sense. Utilizing efficient first-order algorithms to solve discretized problems to moderate accuracy is sufficient. Then a heterogeneous alternating direction method of multipliers (hADMM) is proposed. Different from the classical ADMM, our hADMM adopts two different weighted norms in two subproblems respectively. Additionally, to get more accurate solution, a two-phase strategy is presented, in which the primal-dual active set (PDAS) method is used as a postprocessor of the hADMM. Numerical results not only verify error estimates but also show the efficiency of the hADMM and the two-phase strategy.

DOI : 10.1051/cocv/2018019
Classification : 49J20, 49N05, 65G99, 68W15
Mots-clés : Optimal control, Pointwise state constraints, Lavrentiev regularization, Error estimates, Heterogeneous ADMM, Two-phase strategy
Chen, Zixuan 1 ; Song, Xiaoliang 1 ; Zhang, Xuping 1 ; Yu, Bo 1

1
@article{COCV_2019__25__A5_0,
     author = {Chen, Zixuan and Song, Xiaoliang and Zhang, Xuping and Yu, Bo},
     title = {A {FE-ADMM} algorithm for {Lavrentiev-regularized} state-constrained elliptic control problem},
     journal = {ESAIM: Control, Optimisation and Calculus of Variations},
     publisher = {EDP-Sciences},
     volume = {25},
     year = {2019},
     doi = {10.1051/cocv/2018019},
     zbl = {1437.49003},
     mrnumber = {3943363},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/cocv/2018019/}
}
TY  - JOUR
AU  - Chen, Zixuan
AU  - Song, Xiaoliang
AU  - Zhang, Xuping
AU  - Yu, Bo
TI  - A FE-ADMM algorithm for Lavrentiev-regularized state-constrained elliptic control problem
JO  - ESAIM: Control, Optimisation and Calculus of Variations
PY  - 2019
VL  - 25
PB  - EDP-Sciences
UR  - http://www.numdam.org/articles/10.1051/cocv/2018019/
DO  - 10.1051/cocv/2018019
LA  - en
ID  - COCV_2019__25__A5_0
ER  - 
%0 Journal Article
%A Chen, Zixuan
%A Song, Xiaoliang
%A Zhang, Xuping
%A Yu, Bo
%T A FE-ADMM algorithm for Lavrentiev-regularized state-constrained elliptic control problem
%J ESAIM: Control, Optimisation and Calculus of Variations
%D 2019
%V 25
%I EDP-Sciences
%U http://www.numdam.org/articles/10.1051/cocv/2018019/
%R 10.1051/cocv/2018019
%G en
%F COCV_2019__25__A5_0
Chen, Zixuan; Song, Xiaoliang; Zhang, Xuping; Yu, Bo. A FE-ADMM algorithm for Lavrentiev-regularized state-constrained elliptic control problem. ESAIM: Control, Optimisation and Calculus of Variations, Tome 25 (2019), article no. 5. doi : 10.1051/cocv/2018019. http://www.numdam.org/articles/10.1051/cocv/2018019/

[1] J.J. Alibert and J.P. Raymond, Boundary control of semilinear elliptic equations with discontinuous leading coefficients and unbounded controls. Numer. Func. Anal. Opt. 18 (1997) 235–250. | DOI | MR | Zbl

[2] A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2 (2009) 183–202. | DOI | MR | Zbl

[3] M. Bergounioux and K. Kunisch, Augemented Lagrangian techniques for elliptic state constrained optimal control problems. SIAM J. Control Optim. 35 (1997) 1524–1543. | DOI | MR | Zbl

[4] M. Bergounioux and K. Kunisch, Primal-dual strategy for state-constrained optimal control problems. Comput. Optim. Appl. 22 (2002) 193–224. | DOI | MR | Zbl

[5] S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3 (2011) 1–122. | DOI | Zbl

[6] C. Carstensen, Quasi-interpolation and a posteriori error analysis in finite element methods. ESAIM: M2AN 33 (1999) 1187–1202. | DOI | Numdam | MR | Zbl

[7] E. Casas, L2 estimates for the finite element method for the Dirichlet problem with singular data. Numer. Math. 47 (1985) 627–632. | DOI | MR | Zbl

[8] E. Casas, Boundary control of semilinear elliptic equations with pointwise state constraints. SIAM J. Control Optim. 31 (1993) 993–1006. | DOI | MR | Zbl

[9] T.F. Chan and R. Glowinski, Finite Element Approximation and Iterative Solution of a Class of Mildly Non-Linear Elliptic Equations. Computer Science Department, Stanford University, Stanford (1978).

[10] L. Chen, iFEM: An Integrated Finite Element Methods Package in MATLAB. Technical Report. University of California at Irvine, Irvine (2009).

[11] L. Chen, D.F. Sun and K.C. Toh, An efficient inexact symmetric Gauss–Seidel based majorized ADMM for high-dimensional convex composite conic programming. Math. Program. 161 (2017) 237–270. | DOI | MR | Zbl

[12] K. Deckelnick and M. Hinze, Numerical analysis of a control and state constrained elliptic control problem with piecewise constant control approximations. Numer. Math. Adv. Appl. Springer (2008) 597–604. | DOI | MR | Zbl

[13] J.C. De Los Reyes, C. Meyer and B. Vexler, Finite element error analysis for state-constrained optimal control of the Stokes equations. WIAS (2008). | MR | Zbl

[14] J. Eckstein and D.P. Bertsekas, On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Program. 55 (1992) 293–318. | DOI | MR | Zbl

[15] M. Fazel, T.K. Pong, D.F. Sun and P. Tseng, Hankel matrix rank minimization with applications to system identification and realization. SIAM J. Matrix Anal. Appl. 34 (2013) 946–977. | DOI | MR | Zbl

[16] D. Gabay and B. Mercier, A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput. Math. Appl. 2 (1976) 17–40. | Zbl

[17] W. Gong and N. Yan, A mixed finite element scheme for optimal control problems with pointwise state constraints. J. Sci. Comput. 46 (2011) 182–203. | DOI | MR | Zbl

[18] P. Grisvard, Elliptic problems in nonsmooth domains. Pitman Advanced Pub. Program (1985). | MR | Zbl

[19] M. Hintermüller and K. Kunisch, Feasible and noninterior path-following in constrained minimization with low multiplier regularity. SIAM J. Control Optim. 45 (2006) 1198–1221. | DOI | MR | Zbl

[20] M. Hintermüller and K. Kunisch, Path-following methods for a class of constrained minimization problems in function space. SIAM J. Optim. 17 (2006) 159–187. | DOI | MR | Zbl

[21] M. Hintermüller, K. Ito and K. Kunisch, The primal-dual active set strategy as a semismooth Newton method. SIAM J. Optim. 13 (2002) 865–888. | DOI | MR | Zbl

[22] M. Hinze, A variational discretization concept in control constrained optimization: the linear-quadratic case. Comput. Optim. Appl. 30 (2005) 45–61. | DOI | MR | Zbl

[23] M. Hinze and C. Meyer, Variational discretization of Lavrentiev-regularized state constrained elliptic optimal control problems. Comput. Optim. Appl. 46 (2010) 487–510. | DOI | MR | Zbl

[24] M. Hinze, R. Pinnau, M. Ulbrich and S. Ulbrich. Optimization with PDE Constraints. Springer, Netherlands (2009). | MR | Zbl

[25] K. Ito and K. Kunisch, Semi-smooth Newton methods for state-constrained optimal control problems. Syst. Control Lett. 50 (2003) 221–228. | DOI | MR | Zbl

[26] K.F. Jiang, D.F. Sun and K.C. Toh, An inexact accelerated proximal gradient method for large scale linearly constrained convex SDP. SIAM J. Optim. 22 (2012) 1042–1064. | DOI | MR | Zbl

[27] K. Kunisch, K. Liang and X. Lu, Optimal control for an elliptic system with polygonal state constraints. SIAM J. Control Optim. 48 (2010) 5053–5072. | DOI | MR | Zbl

[28] X.D. Li, D.F. Sun and K.C. Toh, QSDPNAL: A Two-Phase Newton-CG Proximal Augmented Lagrangian Method for Convex Quadratic Semidefinite Programming Problems. Preprint (2015). | arXiv | MR

[29] X.D. Li, D.F. Sun and K.C. Toh, A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions. Math. Program. 155 (2016) 333–373. | DOI | MR | Zbl

[30] W.B. Liu and N.N. Yan, Adaptive Finite Element Methods for Optimal Control Governed by PDEs. Science Press, Beijing (2008).

[31] C. Meyer, U. Prüfert and F. Tröltzsch, On two numerical methods for state-constrained elliptic control problems. Optim. Method Softw. 22 (2007) 871–899. | DOI | MR | Zbl

[32] C. Meyer, A. Rösch and F. Tröltzsch, Optimal Control of PDEs with regularized pointwise state constraints. Comput. Optim. Appl. 33 (2006) 209–228. | DOI | MR | Zbl

[33] G.C. Philippe, The Finite Element Method for Elliptic Problems. North-Holland Publ. Company (1978). | MR

[34] J.W. Pearson, S. Martin and A.J. Wathen, Preconditioners for state-constrained optimal control problems with Moreau-Yosida penalty function. Numer. Linear Algebra Appl. 21 (2014) 81–97. | DOI | MR | Zbl

[35] M. Porcelli, V. Simoncini and M. Stoll, Preconditioning PDE-Constrained Optimization with L1-Sparsity and Control Constraints. Preprint (2016). | arXiv | MR

[36] U. Prüfert, F. Tröltzsch and M. Weiser, The convergence of an interior point method for an elliptic control problem with mixed control-state constraints. Comput. Optim. Appl. 39 (2008) 183–218. | DOI | MR | Zbl

[37] Z. Wu, J. Yin and C. Wang, Elliptic and parabolic equations. In vol. 118 of Springer Proceedings in Mathematics & Statistics (1994). | Zbl

[38] A. Schindele and A. Borzì, Proximal methods for elliptic optimal control problems with sparsity cost functional. Appl. Math. 7 (2016) 967. | DOI

[39] X.L. Song and B. Yu, A two-phase strategy for control constrained elliptic optimal control problems. Numer. Linear Algebra Appl. (to appear). | MR

[40] K.C. Toh and S. Yun, An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems. Pac. J. Optim. 6 (2010) 615–640. | MR | Zbl

[41] P. Tseng, On accelerated proximal gradient methods for convex–concave optimization. SIAM J. Optim. (2008).

[42] M. Ulbrich, Nonsmooth Newton-like Methods for Variational Inequalities and Constrained Optimization Problems in Function Spaces. Ph.D. thesis, Habilitation thesis, Fakultät für Mathematik, Technische Universität München (2002).

[43] M. Ulbrich, Semismooth newton methods for operator equations in function spaces. SIAM J. Optim. 13 (2002) 805–841. | DOI | MR | Zbl

[44] A.J. Wathen, Realistic eigenvalue bounds for the Galerkin mass matrix. IMA J. Numer. Anal. 7 (1987) 449–457. | DOI | MR | Zbl

Cité par Sources :