A globalized inexact semismooth Newton method for strongly convex optimal control problems

Fuente: arXiv
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Main Author: Wachsmuth, Daniel
Format: Preprint
Published: 2025
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author Wachsmuth, Daniel
author_facet Wachsmuth, Daniel
contents We investigate a globalized inexact semismooth Newton method applied to strongly convex optimization problems in Hilbert spaces. Here, the semismooth Newton method is appplied to the dual problem, which has a continuously differentiable objective. We prove global strong convergence of iterates as well as transition to local superlinear convergence. The latter needs a second-order Taylor expansion involving semismooth derivative concepts. The convergence of the globalized method is demonstrated in numerical examples, for which the local unglobalized method diverges.
format Preprint
id arxiv_https___arxiv_org_abs_2503_21612
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A globalized inexact semismooth Newton method for strongly convex optimal control problems
Wachsmuth, Daniel
Optimization and Control
We investigate a globalized inexact semismooth Newton method applied to strongly convex optimization problems in Hilbert spaces. Here, the semismooth Newton method is appplied to the dual problem, which has a continuously differentiable objective. We prove global strong convergence of iterates as well as transition to local superlinear convergence. The latter needs a second-order Taylor expansion involving semismooth derivative concepts. The convergence of the globalized method is demonstrated in numerical examples, for which the local unglobalized method diverges.
title A globalized inexact semismooth Newton method for strongly convex optimal control problems
topic Optimization and Control
url https://arxiv.org/abs/2503.21612