Shape Optimization with Nonlinear Conjugate Gradient Methods

TL;DR

This paper explores the use of nonlinear conjugate gradient methods for shape optimization, demonstrating their efficiency and effectiveness through numerical experiments.

Contribution

It provides a comprehensive investigation of NCG methods applied to shape optimization, including theoretical background and numerical performance analysis.

Findings

NCG methods are efficient for shape optimization

Numerical results confirm the attractiveness of NCG algorithms

Theoretical background supports the practical effectiveness

Abstract

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.