Adaptative Newton-like Method for Shape Optimization
Résumé
The aim of this work is to introduce an adaptive strategy to monitor the rate of convergence of a Newton-like method in numerical shape optimization. Such superlinear iterative algorithm are often computationally intensive and the rate of convergence depends on how accurate the numerical solution of the state equation is. The model concerns a cost function depending on the curvature of $\partial \Omega$, the boundary of the unknown domain, and the solution of an integral equation.
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