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The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of CVOPs. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality.<\/jats:p>","DOI":"10.1007\/s10898-024-01408-x","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T02:01:44Z","timestamp":1717034504000},"page":"429-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning the efficient frontier of convex vector optimization problems"],"prefix":"10.1007","volume":"90","author":[{"given":"Zachary","family":"Feinstein","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1675-5451","authenticated-orcid":false,"given":"Birgit","family":"Rudloff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"1408_CR1","volume-title":"Infinite Dimensional Analysis: A Hitchhiker\u2019s Guide","author":"CD Aliprantis","year":"2007","unstructured":"Aliprantis, C.D., Border, K.C.: Infinite Dimensional Analysis: A Hitchhiker\u2019s Guide. 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