Inexact Interior-Point Method for PDE-Constrained Nonlinear Optimization

Starting from the inexact interior-point framework from Curtis, Schenk, and Wachter [SIAM J. Sci. Comput. , 32 (2012), pp. 3447--3475], we propose an effective Schur-complement slack-control preconditioner for the full Lagrangian Hessian matrix needed at each Newton iteration. Together they yield a...

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Veröffentlicht in:SIAM journal on scientific computing Jg. 36; H. 3; S. A1251 - A1276
Hauptverfasser: Grote, Marcus J., Huber, Johannes, Kourounis, Drosos, Schenk, Olaf
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Society for Industrial and Applied Mathematics 01.01.2014
Schlagworte:
Active control
Algorithms
Approximation
Inequalities
Inversions
Iterative methods
Mathematical models
Methods
Nonlinearity
Numerical analysis
Optimization
Parameter estimation
Partial differential equations
Robustness
Variables
ISSN:1064-8275, 1095-7197
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Zusammenfassung:Starting from the inexact interior-point framework from Curtis, Schenk, and Wachter [SIAM J. Sci. Comput. , 32 (2012), pp. 3447--3475], we propose an effective Schur-complement slack-control preconditioner for the full Lagrangian Hessian matrix needed at each Newton iteration. Together they yield a scalable, robust, and highly parallel method for the numerical solution of large-scale nonconvex PDE-constrained optimization problems with inequality constraints. Because it uses the full Hessian matrix, modifying it whenever needed, the method not only is globally convergent, but also converges fast locally. Our preconditioner is not tailored to any particular class of PDEs or constraints, but instead judiciously exploits the sparsity structure of the Hessian. Numerical examples from PDE-constrained optimal control, parameter estimation, and full-waveform inversion demonstrate the robustness and efficiency of the method, even in the presence of active inequality constraints.
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ISSN:1064-8275
1095-7197
DOI:10.1137/130921283