--- res: bibo_abstract: - In this article we develop a gradient-based algorithm for the solution of multiobjective optimization problems with uncertainties. To this end, an additional condition is derived for the descent direction in order to account for inaccuracies in the gradients and then incorporated into a subdivision algorithm for the computation of global solutions to multiobjective optimization problems. Convergence to a superset of the Pareto set is proved and an upper bound for the maximal distance to the set of substationary points is given. Besides the applicability to problems with uncertainties, the algorithm is developed with the intention to use it in combination with model order reduction techniques in order to efficiently solve PDE-constrained multiobjective optimization problems.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Sebastian foaf_name: Peitz, Sebastian foaf_surname: Peitz foaf_workInfoHomepage: http://www.librecat.org/personId=47427 orcid: https://orcid.org/0000-0002-3389-793X - foaf_Person: foaf_givenName: Michael foaf_name: Dellnitz, Michael foaf_surname: Dellnitz bibo_doi: 10.1007/978-3-319-64063-1_7 dct_date: 2017^xs_gYear dct_isPartOf: - http://id.crossref.org/issn/1860-949X - http://id.crossref.org/issn/1860-9503 - http://id.crossref.org/issn/9783319640624 - http://id.crossref.org/issn/9783319640631 dct_language: eng dct_title: Gradient-Based Multiobjective Optimization with Uncertainties@ ...