Convergence of a steepest descent algorithm in shape optimisation using <i>W</i><sup>1,∞</sup> functions

Built upon previous work of the authors in Deckelnick et al . [ ESAIM: COCV 28 (2022) 2], we present a general shape optimisation framework based on the method of mappings in the W 1, ∞ topology together with a suitable finite element discretisation. For the numerical solution of the respective discrete shape optimisation problems we propose a steepest descent minimisation algorithm with Armijo-Goldstein stepsize rule. We show that the sequence generated by this descent method globally converges, and under appropriate assumptions also, that every accumulation point of this sequence is a stationary point of the shape functional. Moreover, for the mesh discretisation parameter tending to zero we under appropriate assumptions prove convergence of the discrete stationary shapes in the Hausdorff complementary metric. To illustrate our approach we present a selection of numerical examples for PDE constrained shape optimisation problems, where we include numerical convergence studies which support our analytical findings.

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Publication Details

Journal
ESAIM. Mathematical modelling and numerical analysis
Published
2025-04-14
DOI
https://doi.org/10.1051/m2an/2025033
Citations
1
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.33

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article

Convergence of a steepest descent algorithm in shape optimisation using W1,∞ functions

Klaus Deckelnick, Michael Hinze, Philip J. Herbert
1 citations
ESAIM. Mathematical modelling and numerical analysis
Advanced Numerical Analysis Techniques
1.33
article

Convergence of a steepest descent algorithm in shape optimisation using W1,∞ functions

Klaus Deckelnick, Michael Hinze, Philip J. Herbert
article en
1 citations

Abstract

Built upon previous work of the authors in Deckelnick et al . [ ESAIM: COCV 28 (2022) 2], we present a general shape optimisation framework based on the method of mappings in the W 1, ∞ topology together with a suitable finite element discretisation. For the numerical solution of the respective discrete shape optimisation problems we propose a steepest descent minimisation algorithm with Armijo-Goldstein stepsize rule. We show that the sequence generated by this descent method globally converges, and under appropriate assumptions also, that every accumulation point of this sequence is a stationary point of the shape functional. Moreover, for the mesh discretisation parameter tending to zero we under appropriate assumptions prove convergence of the discrete stationary shapes in the Hausdorff complementary metric. To illustrate our approach we present a selection of numerical examples for PDE constrained shape optimisation problems, where we include numerical convergence studies which support our analytical findings.

ESAIM. Mathematical modelling and numerical analysisVol. 59(3)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 25%
Advanced Numerical Analysis Techniques
1.33
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