Semi–Monotonic Augmented Lagrangians for Optimal Control and Parameter Identification

D. Lukáš () and Z. Dostál ()
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D. Lukáš: VŠB-Technical University of Ostrava
Z. Dostál: VŠB-Technical University of Ostrava

A chapter in Numerical Mathematics and Advanced Applications, 2008, pp 621-628 from Springer

Abstract: Abstract Optimization and inverse problems governed by partial differential equations are often formulated as constrained nonlinear programming problems via the Lagrange formalism. The nonlinearity is treated using the sequential quadratic programming. A numerical solution then hinges on an efficient iterative method for the resulting saddle–point systems. In this paper we apply a semi–monotonic augmented Lagrangians method, recently proposed and analyzed by the second author, for equality and simple–bound constrained quadratic programming subproblems arising from optimal control and parameter identification. Provided multigrid preconditioning of primal and dual space inner products and of the Hessian the algorithm converges at O(1) matrix–vector multiplications. Numerical results are given for applications in image segmentation and 2–dimensional magnetostatics discretized using lowest–order Lagrange finite elements.

Keywords: Image Segmentation; Quadratic Programming; Conjugate Gradient Method; Linear Complexity; Nonlinear Programming Problem (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-69777-0_74

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DOI: 10.1007/978-3-540-69777-0_74

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