Skip to main content
Log in

Elliptic optimal control problems with L 1-control cost and applications for the placement of control devices

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

Abstract

Elliptic optimal control problems with L 1-control cost are analyzed. Due to the nonsmooth objective functional the optimal controls are identically zero on large parts of the control domain. For applications, in which one cannot put control devices (or actuators) all over the control domain, this provides information about where it is most efficient to put them. We analyze structural properties of L 1-control cost solutions. For solving the non-differentiable optimal control problem we propose a semismooth Newton method that can be stated and analyzed in function space and converges locally with a superlinear rate. Numerical tests on model problems show the usefulness of the approach for the location of control devices and the efficiency of our algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Adams, R.A.: Sobolev Spaces. Academic Press, New York (1975)

    MATH  Google Scholar 

  2. Ascher, U.M., Haber, E., Huang, H.: On effective methods for implicit piecewise smooth surface recovery. SIAM J. Sci. Comput. 28(1), 339–358 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bergounioux, M., Ito, K., Kunisch, K.: Primal-dual strategy for constrained optimal control problems. SIAM J. Control Optim. 37(4), 1176–1194 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)

    MATH  Google Scholar 

  5. Chan, T.F., Tai, X.-C.: Identification of discontinuous coefficients in elliptic problems using total variation regularization. SIAM J. Sci. Comput. 25(3), 881–904 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, X., Nashed, Z., Qi, L.: Smoothing methods and semismooth methods for nondifferentiable operator equations. SIAM J. Numer. Anal. 38(4), 1200–1216 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  7. Clarke, F.H.: Optimization and Nonsmooth Analysis. Canadian Mathematical Society Series of Monographs and Advanced Texts. Wiley, New York (1983)

    MATH  Google Scholar 

  8. Costa, L., Figueiredo, I.N., Leal, R., Oliveira, P., Stadler, G.: Modeling and numerical study of actuator and sensor effects for a laminated piezoelectric plate. Comput. Struct. 85(7–8), 385–403 (2007)

    Article  MathSciNet  Google Scholar 

  9. Davis, T.A., Hager, W.W.: A sparse proximal implementation of the LP dual active set algorithm. Math. Program. (2007). doi:10.1007/s10107-006-0017-0

    Google Scholar 

  10. Durand, S., Nikolova, M.: Stability of the minimizers of least squares with a non-convex regularization. I. Local behavior. Appl. Math. Optim. 53(2), 185–208 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Ekeland, I., Temam, R.: Convex Analysis and Variational Problems. North-Holland, Amsterdam (1976)

    MATH  Google Scholar 

  12. Figueiredo, I.N., Leal, C.: A piezoelectric anisotropic plate model. Asymptot. Anal. 44(3–4), 327–346 (2005)

    MathSciNet  MATH  Google Scholar 

  13. Gilberg, T., Trudinger, N.S.: Elliptic Partial Differential Equations of Second Order. Springer, Berlin (1983)

    Google Scholar 

  14. Glowinski, R.: Numerical Methods for Nonlinear Variational Inequalities. Springer, New York (1984)

    Google Scholar 

  15. Grund, T., Rösch, A.: Optimal control of a linear elliptic equation with a supremum-norm functional. Optim. Methods Softw. 15, 299–329 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  16. Hager, W.W.: The dual active set algorithm. In: Pardalos, P.M. (ed.) Advances in Optimization and Parallel Computing, pp. 137–142. North-Holland, Amsterdam (1992)

    Google Scholar 

  17. Hager, W.W.: The dual active set algorithm and its application to linear programming. Comput. Optim. Appl. 21(3), 263–275 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hager, W.W., Hearn, D.W.: Application of the dual active set algorithm to quadratic network optimization. Comput. Optim. Appl. 1(4), 349–373 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hager, W.W., Ianculescu, G.D.: Dual approximations in optimal control. SIAM J. Control Optim. 22(3), 423–465 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  20. Hintermüller, M., Ito, K., Kunisch, K.: The primal-dual active set strategy as a semi-smooth Newton method. SIAM J. Optim. 13(3), 865–888 (2003)

    Article  MATH  Google Scholar 

  21. Hintermüller, M., Kunisch, K.: Feasible and noninterior path-following in constrained minimization with low multiplier regularity. SIAM J. Control Optim. 45(4), 1198–1221 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  22. Hintermüller, M., Ulbrich, M.: A mesh-independence result for semismooth Newton methods. Math. Program. Ser. B 101(1), 151–184 (2004)

    Article  MATH  Google Scholar 

  23. Ito, K., Kunisch, K.: Augmented Lagrangian methods for nonsmooth convex optimization in Hilbert spaces. Nonlinear Anal. TMA 41, 591–616 (2000)

    Article  MathSciNet  Google Scholar 

  24. Ito, K., Kunisch, K.: The primal-dual active set method for nonlinear optimal control problems with bilateral constraints. SIAM J. Control Optim. 43(1), 357–376 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  25. Nikolova, M.: Local strong homogeneity of a regularized estimator. SIAM J. Appl. Math. 61(2), 633–658 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  26. Nikolova, M.: Analysis of the recovery of edges in images and signals by minimizing nonconvex regularized least-squares. Multiscale Model. Simul. 4(3), 960–991 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  27. Ring, W.: Structural properties of solutions to total variation regularization problems. Math. Model. Numer. Anal. 34(4), 799–810 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  28. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)

    MATH  Google Scholar 

  29. Stadler, G.: Semismooth Newton and augmented Lagrangian methods for a simplified friction problem. SIAM J. Optim. 15(1), 39–62 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  30. Tröltzsch, F.: Optimale Steuerung partieller Differentialgleichungen. Vieweg, Wiesbaden (2005)

    MATH  Google Scholar 

  31. Ulbrich, M.: Semismooth Newton methods for operator equations in function spaces. SIAM J. Optim. 13(3), 805–842 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  32. Ulbrich, M., Ulbrich, S.: Superlinear convergence of affine-scaling interior-point Newton methods for infinite-dimensional nonlinear problems with pointwise bounds. SIAM J. Control Optim. 38, 1934–1984 (2000)

    Article  MathSciNet  Google Scholar 

  33. Vogel, C.R.: Computational Methods for Inverse Problems. Frontiers in Applied Mathematics. SIAM, Philadelphia (2002)

    MATH  Google Scholar 

  34. Vossen, G., Maurer, H.: On L 1-minimization in optimal control and applications to robotics. Optim. Control Appl. Methods 27(6), 301–321 (2006)

    Article  MathSciNet  Google Scholar 

  35. Weiser, M.: Interior point methods in function space. SIAM J. Control Optim. 44(5), 1766–1786 (2005)

    Article  MathSciNet  Google Scholar 

  36. Weiser, M., Gänzler, T., Schiela, A.: A control reduced primal interior point method for a class of control constrained optimal control problems. Comput. Optim. Appl. (2007). doi:10.1007/s10589-007-9088-y

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Georg Stadler.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stadler, G. Elliptic optimal control problems with L 1-control cost and applications for the placement of control devices. Comput Optim Appl 44, 159–181 (2009). https://doi.org/10.1007/s10589-007-9150-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-007-9150-9

Keywords

Navigation