PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 19, 20260 citationsOpen Access

A globalized inexact semismooth Newton method for nonsmooth fixed-point equations involving variational inequalities

View Full Paper
AAAmal AlphonseCCConstantin ChristofMHMichael Hintermüller

Key Points

  • The aim is to develop a semismooth Newton method for solving fixed-point equations in Banach spaces.
  • Developed a semismooth Newton framework allowing inexact evaluations.
  • Applied a localization technique for locally contractive fixed-point equations.
  • Used the Banach fixed-point theorem to globalize the algorithm.
  • Achieved q-superlinear convergence under certain conditions.
  • Demonstrated mesh-independence through numerical experiments.
  • Successfully applied the method to a quasi-variational inequality from thermoforming.

Abstract

We develop a semismooth Newton framework for the numerical solution of fixed-point equations that are posed in Banach spaces. The framework is motivated by applications in the field of obstacle-type quasi-variational inequalities and implicit obstacle problems. It is discussed in a general functional analytic setting and allows for inexact function evaluations and Newton steps. Moreover, if a certain contraction assumption holds, we show that it is possible to globalize the algorithm by means of the Banach fixed-point theorem and to ensure q -superlinear convergence to the problem solution for arbitrary starting values. By means of a localization technique, our Newton method can also be used to determine solutions of fixed-point equations that are only locally contractive and not uniquely solvable. We apply our algorithm to a quasi-variational inequality which arises in thermoforming and which not only involves the obstacle problem as a source of nonsmoothness but also a semilinear PDE containing a nondifferentiable Nemytskii operator. Our analysis is accompanied by numerical experiments that illustrate the mesh-independence and q -superlinear convergence of the developed solution algorithm.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Alphonse et al. (2025) studied this question.

synapsesocial.com/papers/6996a83eecb39a600b3eeb93https://doi.org/10.18452/36018
Ask AI
Helpful
Bookmark
Share
View Full Paper