Gradient method with AFEM for parameter-estimation

Authors

  • Roland Becker LMAP, UPPA, Pau, France.

DOI:

https://doi.org/10.17268/sel.mat.2023.01.05

Keywords:

Adaptive finite element methods, parameter estimation, gradient method

Abstract

We consider the adaptive finite element discretization of parameter estimation problems for nonlinear elliptic partial differential equations. The idea is to use a gradient method on the finite-dimensional parameter space for the minimization of the least-squares residual. Since the gradient involves solution of partial differential equations, it is not accesable, and is replaced by an approximation obtained by finite elements.

This results into a perturbed gradient method. We use an (a posteriori) error estimator to control the accuracy of the gradient approximation and propose an algorithm, which links the estimator to the progress of the iteration. We show convergence of the algorithm under typical structural assumptions.

References

Becker R, Innerberger M, Praetorius D. Adaptive FEM for Parameter-Errors in Elliptic Linear-Quadratic Parameter Estimation Problems. SIAM J. Numer. Anal. 2022; 60(3):1450–1471.

Becker R. Estimating the control error in discretized pde-constrained optimization. J. of Numerical Mathematics. 2006; 14(3):163–185.

Babuska I, Rheinboldt W. Error estimates for adaptive finite element computations. SIAM J. Numer. Anal. 1978; 15:736–754.

Eriksson K, Johnson C. An adaptive finite element method for linear elliptic problems. Math. Comp. 1988; 50(182):361–383.

Verfürth R. A Review of A Posteriori Error Estimation and Adaptive Mesh-Refinement Techniques. Wiley/Teubner, New York-Stuttgart, 1996.

Carstensen C, Feischl M, Page M, Praetorius D. Axioms of adaptivity. Comput. Math. Appl. 2014; 67(6):1195–1253.

Gantner G, Praetorius D. Plain convergence of adaptive algorithms without exploiting reliability and efficiency. IMA J. Numer. Anal. 2022; 42(2): 1434–1453.

Nocedal J, Wright SJ. Numerical optimization. Springer Series in Operations Research and Financial Engineering, Springer, New York, second ed., 2006.

Nesterov Y. Lectures on convex optimization. Second edition of [ MR2142598]: vol. 137 of Springer Optimization and Its Applications. Springer, Cham, 2018.

Becker R, Brunner M, Innerberger M, Melenk JM, Praetorius D. Rate-optimal goal-oriented adaptive FEM for semilinear elliptic PDEs. Comput. Math. Appl. 2022; 118:18–35.

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Published

2023-06-14

How to Cite

Becker, R. (2023). Gradient method with AFEM for parameter-estimation. Selecciones Matemáticas, 10(01), 51 - 59. https://doi.org/10.17268/sel.mat.2023.01.05