Sebastian Blauth
Published 2022-01-14, updated 2022-03-30Version 2
In this chapter, we investigate recently proposed nonlinear conjugate gradient (NCG) methods for shape optimization problems. We briefly introduce the methods as well as the corresponding theoretical background and investigate their performance numerically. The obtained results confirm that the NCG methods are efficient and attractive solution algorithms for shape optimization problems.
A Newton derivative scheme for shape optimization problems constrained by variational inequalities
Nonlinear Conjugate Gradient Methods for Optimization of Set-Valued Mappings of Finite Cardinality
Shape optimization problems for functionals with a boundary integral
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