Abstract
In the paper we show how, based on the preconditioned Krylov subspace methods, to compute the covariance matrix of parameter estimates, which is crucial for efficient methods of optimum experimental design.
Mathematics Subject Classification (2000). Primary 65K10; Secondary 15A09,65F30.
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References
A.C. Atkinson and A.N. Donev. Optimum Experimental Designs. Oxford University Press, 1992.
S.P. Asprey and S. Macchietto. Statistical tools for optimal dynamic model building. Computers and Chemical Engineering, 24:1261–1267, 2000.
U. Ascher. Collocation for two-point boundary value problems revisited. SIAM Journal on Numerical Analysis, 23(3):596–609, 1986.
A. Battermann and E.W. Sachs. Block preconditioners for KKT systems in PDEgoverned optimal control problems. In K.H. Hoffman, R.H.W. Hoppe, and V. Schulz, editors, Fast solution of discretized optimization problems, 1–18. ISNM, Int. Ser. Numer. Math. 138, 2001.
I. Bauer, H.G. Bock, S. Körkel, and J.P. Schlöder. Numerical methods for initial value problems and derivative generation for DAE models with application to optimum experimental design of chemical processes. In F. Keil, W. Mackens, H. Voss, and J. Werther, editors, Scientific Computing in Chemical Engineering II, volume 2, 282–289. Springer, Berlin-Heidelberg, 1999.
I. Bauer, H.G. Bock, S. Körkel, and J.P. Schlöder. Numerical methods for optimum experimental design in DAE systems. Journal of Computational and Applied Mathematics, 120:1–25, 2000.
H.G. Bock. Numerical treatment of inverse problems in chemical reaction kinetics. In K.-H. Ebert, P. Deuflhard, and W. Jäger, editors, Modelling of Chemical Reaction Systems, volume 18 of Springer Series in Chemical Physics, pages 102–125. Springer Verlag, 1981.
H.G. Bock. Randwertproblemmethoden zur Parameteridentifizierung in Systemen nichtlinearer Differentialgleichungen, volume 183 of Bonner Mathematische Schriften. University of Bonn, 1987.
H.G. Bock, E.A. Kostina, and O.I. Kostyukova. Conjugate gradient methods for computing covariance matrices for constrained parameter estimation problems. SIAM Journal on Matrix Analysis and Application, 29:626–642, 2007.
R. Bulirsch. Die Mehrzielmethode zur numerischen Lösung von nichtlinearen Randwertproblemen und Aufgaben der optimalen Steuerung. Technical report, Carl- Cranz-Gesellschaft, 1971.
T. Carraro. Parameter estimation and optimal experimental design in flow reactors. Phd thesis, University of Heidelberg, 2005.
T. Carraro, V. Heuveline, and R. Rannacher. Determination of kinetic parameters in laminar flow reactors. I. Numerical aspects. In W. Jäger, R. Rannacher, and J. Warnatz, editors, Reactive Flows, Diffusion and Transport. From Experiments via Mathematical Modeling to Numerical Simulation and Optimization. Springer, 2007.
A. Cervantes and L.T. Biegler. Large-scale DAE optimization using a simultaneous NLP formulation. AIChE Journal, 44(5):1038–1050, 2004.
H.S. Dollar, and A.J. Wathen. Approximate factorization constraint preconditioners for saddle-point matrices. Siam J. Sci. Comput., 27(5): 1555–1572, 2006.
V.V. Fedorov. Theory of Optimal Experiments. Probability and Mathematical Statistics. Academic Press, London, 1972.
A. Griewank. Evaluating Derivatives. Principles and Techniques of Algorithmic Differentiation. Frontiers in Applied Mathematics. SIAM, 2000.
E. Haber, L. Horesh, and L. Tenorio. Numerical methods for optimal experimental design of large-scale ill-posed problems. Inverse Problems. 24(5), 2008.
C.T. Kelley, Iterative Methods for Linear and Nonlinear Equations, SIAM, Philadelphia, 1995.
S. Körkel and E.A. Kostina. Numerical methods for nonlinear experimental design. In H.G. Bock, E.A. Kostina, H.X. Phu, and R. Rannacher, editors, Modeling, Simulation and Optimization of Complex Processes, Proceedings of the International Conference on High Performance Scientific Computing, 2003, Hanoi, Vietnam. Springer, 2004.
S. Körkel. Numerische Methoden für Optimale Versuchsplanungsprobleme bei nichtlinearen DAE-Modellen. Phd thesis, Universität Heidelberg, 2002.
E. Kostina, M. Saunders, and I. Schierle. Computation of covariance matrices for constrained parameter estimation problems using LSQR. Technical Report, Department of Mathematics and Computer Science, U Marburg, 2008.
C.C. Paige and M.A. Saunders, LSQR: An algorithm for sparse linear equations and sparse least-squares, ACM Trans. Math. Softw., 8(1):43–71, 1982.
C.C. Paige and M.A. Saunders, LSQR: Sparse linear equations and least-squares, ACM Trans. Math. Softw., 8(2):195–209, 1982.
F. Pukelsheim. Optimal Design of Experiments. John Wiley & Sons, Inc., New York, 1993.
T. Rees, H.S. Dollar, and A.J. Wathen. Optimal solvers for PDE-constrained optimization. Technical report RAL-TR-2008-018, Rutherford Appleton Laboratory, 2008.
P.E. Rudolph and G. Herrendörfer. Optimal experimental design and accuracy of parameter estimation for nonlinear regression models used in long-term selection. Biom. J., 37(2):183–190, 1995.
I. Schierle. Computation of Covariance Matrices for Constrained Nonlinear Parameter Estimation Problems in Dynamic Processes Using iterative Linear Algebra Methods. Diploma thesis, Universität Heidelberg, 2008.
V.H. Schulz. Ein effizientes Kollokationsverfahren zur numerischen Behandlung von Mehrpunktrandwertaufgaben in der Parameteridentifizierung und Optimalen Steuerung. Diploma thesis, Universität Augsburg, 1990.
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Kostina, E., Kostyukova, O. (2012). Computing Covariance Matrices for Constrained Nonlinear Large Scale Parameter Estimation Problems Using Krylov Subspace Methods. In: Leugering, G., et al. Constrained Optimization and Optimal Control for Partial Differential Equations. International Series of Numerical Mathematics, vol 160. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-0133-1_11
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DOI: https://doi.org/10.1007/978-3-0348-0133-1_11
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