---
res:
  bibo_abstract:
  - In this article, we build on previous work to present an optimization algorithm
    for nonlinearly constrained multi-objective optimization problems. The algorithm
    combines a surrogate-assisted derivative-free trust-region approach with the filter
    method known from single-objective optimization. Instead of the true objective
    and constraint functions, so-called fully linear models are employed and we show
    how to deal with the gradient inexactness in the composite step setting, adapted
    from single-objective optimization as well. Under standard assumptions, we prove
    convergence of a subset of iterates to a quasi-stationary point and if constraint
    qualifications hold, then the limit point is also a KKT-point of the multi-objective
    problem.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Manuel Bastian
      foaf_name: Berkemeier, Manuel Bastian
      foaf_surname: Berkemeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=51701
  - foaf_Person:
      foaf_givenName: Sebastian
      foaf_name: Peitz, Sebastian
      foaf_surname: Peitz
      foaf_workInfoHomepage: http://www.librecat.org/personId=47427
    orcid: 0000-0002-3389-793X
  dct_date: 2022^xs_gYear
  dct_language: eng
  dct_title: Multi-Objective Trust-Region Filter Method for Nonlinear Constraints
    using Inexact Gradients@
...