{"language":[{"iso":"eng"}],"publication_status":"published","doi":"10.1007/978-3-319-96104-0_2","publication":"Numerical and Evolutionary Optimization – NEO 2017","conference":{"name":"NEO 2017: Numerical and Evolutionary Optimization"},"type":"conference","date_updated":"2022-01-06T07:04:00Z","status":"public","place":"Cham","year":"2018","author":[{"first_name":"Bennet","full_name":"Gebken, Bennet","id":"32643","last_name":"Gebken"},{"full_name":"Peitz, Sebastian","last_name":"Peitz","id":"47427","orcid":"https://orcid.org/0000-0002-3389-793X","first_name":"Sebastian"},{"full_name":"Dellnitz, Michael","last_name":"Dellnitz","first_name":"Michael"}],"abstract":[{"text":"In this article we propose a descent method for equality and inequality constrained multiobjective optimization problems (MOPs) which generalizes the steepest descent method for unconstrained MOPs by Fliege and Svaiter to constrained problems by using two active set strategies. Under some regularity assumptions on the problem, we show that accumulation points of our descent method satisfy a necessary condition for local Pareto optimality. Finally, we show the typical behavior of our method in a numerical example.","lang":"eng"}],"department":[{"_id":"101"}],"publication_identifier":{"issn":["1860-949X","1860-9503"],"isbn":["9783319961033","9783319961040"]},"title":"A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems","_id":"8750","citation":{"ieee":"B. Gebken, S. Peitz, and M. Dellnitz, “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems,” in <i>Numerical and Evolutionary Optimization – NEO 2017</i>, 2018.","bibtex":"@inproceedings{Gebken_Peitz_Dellnitz_2018, place={Cham}, title={A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-96104-0_2\">10.1007/978-3-319-96104-0_2</a>}, booktitle={Numerical and Evolutionary Optimization – NEO 2017}, author={Gebken, Bennet and Peitz, Sebastian and Dellnitz, Michael}, year={2018} }","mla":"Gebken, Bennet, et al. “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems.” <i>Numerical and Evolutionary Optimization – NEO 2017</i>, 2018, doi:<a href=\"https://doi.org/10.1007/978-3-319-96104-0_2\">10.1007/978-3-319-96104-0_2</a>.","ama":"Gebken B, Peitz S, Dellnitz M. A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In: <i>Numerical and Evolutionary Optimization – NEO 2017</i>. Cham; 2018. doi:<a href=\"https://doi.org/10.1007/978-3-319-96104-0_2\">10.1007/978-3-319-96104-0_2</a>","apa":"Gebken, B., Peitz, S., &#38; Dellnitz, M. (2018). A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In <i>Numerical and Evolutionary Optimization – NEO 2017</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-319-96104-0_2\">https://doi.org/10.1007/978-3-319-96104-0_2</a>","short":"B. Gebken, S. Peitz, M. Dellnitz, in: Numerical and Evolutionary Optimization – NEO 2017, Cham, 2018.","chicago":"Gebken, Bennet, Sebastian Peitz, and Michael Dellnitz. “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems.” In <i>Numerical and Evolutionary Optimization – NEO 2017</i>. Cham, 2018. <a href=\"https://doi.org/10.1007/978-3-319-96104-0_2\">https://doi.org/10.1007/978-3-319-96104-0_2</a>."},"user_id":"47427","date_created":"2019-03-29T13:26:47Z"}