Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-24T12:22:03.040Z Has data issue: false hasContentIssue false

Dual-weighted goal-oriented adaptive finite elements for optimal control of elliptic variational inequalities

Published online by Cambridge University Press:  27 March 2014

M. Hintermüller
Affiliation:
Department of Mathematics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. [email protected]; [email protected]
R.H.W. Hoppe
Affiliation:
Department of Mathematics, University of Houston, Houston, TX 77204-3008, USA; [email protected] Institute of Mathematics, University of Augsburg, Universitätsstraße 14, 86152 Augsburg, Germany; [email protected]
C. Löbhard
Affiliation:
Department of Mathematics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. [email protected]; [email protected]
Get access

Abstract

A dual-weighted residual approach for goal-oriented adaptive finite elements for a class of optimal control problems for elliptic variational inequalities is studied. The development is based on the concept of C-stationarity. The overall error representation depends on primal residuals weighted by approximate dual quantities and vice versa as well as various complementarity mismatch errors. Also, a priori bounds for C-stationary points and associated multipliers are derived. Details on the numerical realization of the adaptive concept are provided and a report on numerical tests including the critical cases of biactivity are presented.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

K. Atkinson and W. Han, Theoretical Numerical Analysis: A Functional Analysis Framework. Springer (2009).
Anitescu, M., Tseng, P. and Wright, S.J., Elastic-mode algorithms for mathematical programs with equilibrium constraints: global convergence and stationarity properties. Math. Program 110 (2005) 337371. Google Scholar
Bänsch, E., Local mesh refinement in 2 and 3 dimensions. IMPACT Comput. Sci. Engrg. 3 (1991) 181198. Google Scholar
V. Barbu, Optimal Control of Variational Inequalities. Pitman, Boston, London, Melbourne (1984).
Braess, D., Carstensen, C. and Hoppe, R.H.W., Convergence analysis of a conforming adaptive finite element method for an obstacle problem. J. Numer. Math. 107 (2007) 455471. Google Scholar
Braess, D., Carstensen, C. and Hoppe, R.H.W., Error reduction in adaptive finite element approximations of elliptic obstacle problems. J. Comput. Math. 27 (2009) 148169. Google Scholar
R. Becker, H. Kapp and R. Rannacher, Adaptive finite element methods for optimal control of partial differential equations: Basic concept. SIAM J. Control Optim. (2000).
W. Bangerth and R. Rannacher, Adaptive Finite Element Methods for Differential Equations. Birkhäuser Verlag, Basel (2003).
Braess, D., A posteriori error estimators for obstacle problems – another look. Numer. Math. 101 (2005) 415421. Google Scholar
Benedix, O. and Vexler, B., A posteriori error estimation and adaptivity for elliptic optimal control problems with state constraints. Comput. Optim. Appl. 44 (2009) 325. Google Scholar
I. Babuška, J. Whiteman and T. Strouboulis, Finite Elements: An Introduction to the Method and Error Estimation. Oxford University Press (2011).
Carstensen, C., An adaptive mesh-refining algorithm allowing for an stable projection onto Courant finite element spaces. Constructive Approximation 20 (2004) 549564. Google Scholar
Ferris, M.C. and Munson, T.S., Interfaces to path 3.0: Design, implementation and usage. Comput. Optim. Appl. 12 207227 (1999). Google Scholar
F. Facchinei and J.S. Pang. Finite-Dimensional Variational Inequalities and Complementarity Problems, in vol. 1 of Springer Ser. Oper. Research. Springer (2003).
Günther, A. and Hinze, M., A posteriori error control of a state constrained elliptic control problem. J. Numer. Math. 16 (2008) 307322. Google Scholar
Hintermüller, M. and Hoppe, R.H.W., Goal-oriented adaptivity in control constrained optimal control of partial differential equations. SIAM J. Control Optim. 47 (2008) 17211743. Google Scholar
Hintermüller, M. and Hoppe, R.H.W., Goal-oriented adaptivity in pointwise state constrained optimal control of partial differential equations. SIAM J. Control Optim. 48 (2010) 54685487. Google Scholar
M. Hintermüller and R.H.W. Hoppe, Goal-oriented mesh adaptivity for mixed control-state constrained elliptic optimal control problems, in vol. 15 of Appl. Numer. Partial Differ. Eq., edited by W. Fitzgibbon, Y.A. Kuznetsov, P. Neittaanmäki and J. Périaux. Comput. Methods Appl. Sci. Springer, Berlin-Heidelberg-New York (2010) 97–111.
Hintermüller, M., Hinze, M. and Tber, M.H., An adaptive finite-element Moreau-Yosida-based solver for a non-smooth CahnHilliard problem. Optim. Methods Software 26 (2011) 777811. Google Scholar
Hintermüller, M., Ito, K. and Kunisch., K. The primal-dual active set strategy as a semismooth Newton method. SIAM J. Optim. 13 (2002) 865888. Google Scholar
Hintermüller, M. and Kopacka, I.. Mathematical programs with complementarity constraints in function space: C- and strong stationarity and a path-following algorithm. SIAM J. Optim. 20 (2009) 868902. Google Scholar
Hintermüller, M. and Kopacka, I., A smooth penalty approach and a nonlinear multigrid algorithm for elliptic MPECs. Comput. Optim. Appl. 50 (2011) 111145. DOI: 10.1007/s10589-009-9307-9. Google Scholar
M. Hintermüller and T. Surowiec, A bundle-free implicit programming approach for a class of MPECs in function space. Preprint (2012).
Johnson, C., Adaptive finite element methods for the obstacle problem. Math. Models Methods Appl. Sci. 2 (1992) 483487. Google Scholar
D. Klatte and B. Kummer, Nonsmooth Equations in Optimization: Regularity, Calculus, Methods and Applications. Nonconvex Optim. Appl. Kluwer Academic (2002).
D. Kinderlehrer and G. Stampacchia, An Introduction to Variational Inequalities and Their Applications. Academic Press, New York (1980).
Kunisch, K. and Wachsmuth, D.. Path-following for optimal control of stationary variational inequalities. Comput. Optim. Appl. 51 (2012) 13451373. Google Scholar
Z.Q. Luo, J.S. Pang and D. Ralph, Mathematical Programs with Equilibrium Constraints. Cambridge University Press (1996).
Liu, W. and Yan, N., A posteriori error estimates for distributed convex optimal control problems. Advances Comput. Math. 15 (2001) 285309. Google Scholar
B.S. Mordukhovich, Variational Analysis and Generalized Differentiation I: Basic Theory, vol. 330 of Grundlehren der mathematischen Wissenschaften. Springer (2006).
B.S. Mordukhovich, Variational Analysis and Generalized Differentiation II: Applications, vol. 331 of Grundlehren der mathematischen Wissenschaften. Springer (2006).
Mignot, F. and Puel, J.P.. Optimal control in some variational inequalities. SIAM J. Control Optim. 22 (1984) 466476. Google Scholar
P. Neittaanmäki, J. Sprekels and D. Tiba, Optimization of Elliptic Systems: Theory and Applications. Springer Monogr. Math. Springer (2006).
Nochetto, R.H., Siebert, K.G. and Veeser, A., Pointwise a posteriori error control for elliptic obstacle problems. Numer. Math. 95 (2003) 163195. DOI: 10.1007/s00211-002-0411-3. Google Scholar
J.V. Outrata, M. Kočvara and J. Zowe, Nonsmooth Approach to Optimization Problems with Equilibrium Constraints: Theory, Applications, and Numerical Results, vol. 152 of Nonconvex Optim. Appl. Kluwer Academic Publishers (1998).
Ralph, D.. Global convergence of damped Newton’s method for nonsmooth equations via the path search. Math. Oper. Res. 19 (1994) 352389. Google Scholar
S.I. Repin, A Posteriori Estimates for Partial Differential Equations. Radon Ser. Comput. Appl. Math. De Gruyter (2008).
J.-F. Rodrigues, Obstacle Problems in Mathematical Physics. North-Holland, Amsterdam (1987).
Rösch, A. and Wachsmuth, D., A posteriori error estimates for optimal control problems with state and control constraints. Numer. Math. 120 (2012) 733762. Google Scholar
Scheel, H. and Scholtes, S., Mathematical programs with complementarity constraints: stationarity, optimality, and sensitivity. Math. Oper. Research 25 (2000) 122. Google Scholar
Veeser, A., Efficient and reliable a posteriori error estimators for elliptic obstacle problems. SIAM J. Numer. Anal. 39 (2001) 146167. Google Scholar
R. Verfürth, A review of a posteriori error estimation and adaptive mesh-refinement techniques. Wiley-Teubner (1996).
Vexler, B. and Wollner, W., Adaptive finite elements for elliptic optimization problems with control constraints. SIAM J. Control Optim. 47 (2008) 509534. Google Scholar