Summary
Multigrid optimization schemes that solve elliptic linear and bilinear optimal control problems are discussed. For the solution of these problems, the multigrid for optimization (MGOPT) method and the collective smoothing multigrid (CSMG) method are developed and compared. It is shown that though these two methods are formally similar, they provide different approaches to computational optimization with partial differential equations.
Similar content being viewed by others
References
Borzì A. (2003). Multigrid methods for parabolic distributed optimal control problems. J Comput Appl Math 157: 365–382
Borzì A. (2005). A multigrid scheme for elliptic constrained optimal control problems. Comput Optim Appl 31: 309–333
Borzì A. (2005). On the convergence of the MG/OPT method. PAMM 5: 735–736
Borzì A. (2007). High-order discretization and multigrid solution of elliptic nonlinear constrained optimal control problems. J Comput Appl Math 200: 67–85
Borzì A., Kunisch K. and Kwak D. (2003). Accuracy and convergence properties of the finite difference multigrid solution of an optimal control optimality system. SIAM J Control Optim 41: 1477–1497
Brandt A. (1977). Multi-level adaptive solutions to boundary-value problems. Math Comp 31: 333–390
Georgescu V. (1979). On the unique continuation property for Schrödinger Hamiltonians. Helv Phys Acta 52: 655–670
Gunzburger M.D., Hou L. and Svobodny T.P. (1991). Finite element approximations of an optimal control problem associated with the scalar Ginzburg–Landau equation. Comput Math Appl 21: 123–131
Lewis, R. M., Nash, S. G.: A multigrid approach to the optimization of systems governed by differential equations. AIAA-2000-4890, 8th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA (2000)
Lewis R.M. and Nash S.G. (2005). Model problems for the multigrid optimization of systems governed by differential equations. SIAM J Sci Comput 26: 1811–1837
Lions J.L. (1971). Optimal control of systems governed by partial differential equations. Springer, Berlin
Nash S.G. (2000). A multigrid approach to discretized optimization problems. Optim Methods Softw 14: 99–116
Nocedal J. and Wright S.J. (1999). Numerical optimization. Springer, New York
Oh S., Bouman C. and Webb K. (2006). Multigrid tomographic inversion with variable resolution data and image spaces. IEEE Trans Image Proc 15: 2805–2819
Oh S., Milstein A., Bouman C. and Webb K. (2005). A general framework for nonlinear multigrid inversion. IEEE Trans Image Proc 14: 125–140
Oh S., Milstein A., Bouman C. and Webb K. (2005). Multigrid algorithms for optimization and inverse problems. IEEE Trans Image Proc 14: 125–140
Ye J.C., Bouman C., Webb K. and Millane R. (2001). Nonlinear multigrid algorithms for bayesian optical diffusion tomography. IEEE Trans Image Proc 10(6): 909–922
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vallejos, M., Borzì, A. Multigrid optimization methods for linear and bilinear elliptic optimal control problems. Computing 82, 31–52 (2008). https://doi.org/10.1007/s00607-008-0261-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-008-0261-7