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Graph."],"published-print":{"date-parts":[[2018,8,31]]},"abstract":"<jats:p>Typical design for manufacturing applications requires simultaneous optimization of conflicting performance objectives: Design variations that improve one performance metric may decrease another performance metric. In these scenarios, there is no unique optimal design but rather a set of designs that are optimal for different trade-offs (called Pareto-optimal). In this work, we propose a novel approach to discover the Pareto front, allowing designers to navigate the landscape of compromises efficiently. Our approach is based on a first-order approximation of the Pareto front, which allows entire neighborhoods rather than individual points on the Pareto front to be captured. In addition to allowing for efficient discovery of the Pareto front and the corresponding mapping to the design space, this approach allows us to represent the entire trade-off manifold as a small collection of patches that comprise a high-quality and piecewise-smooth approximation. We illustrate how this technique can be used for navigating performance trade-offs in computer-aided design (CAD) models.<\/jats:p>","DOI":"10.1145\/3197517.3201385","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T15:56:23Z","timestamp":1533052583000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Interactive exploration of design trade-offs"],"prefix":"10.1145","volume":"37","author":[{"given":"Adriana","family":"Schulz","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harrison","family":"Wang","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eitan","family":"Grinspun","sequence":"additional","affiliation":[{"name":"Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Solomon","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Matusik","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Shailen Agrawal and Michiel van de Panne. 2013. Pareto Optimal Control for Natural and Supernatural Motions. (2013).  Shailen Agrawal and Michiel van de Panne. 2013. Pareto Optimal Control for Natural and Supernatural Motions. (2013).","DOI":"10.1145\/2522628.2522902"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766985"},{"key":"e_1_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Sunith Bandaru and Kalyanmoy Deb. 2015. Temporal innovization: Evolution of design principles using multi-objective optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) Vol. 9018. Springer Verlag 79--93.  Sunith Bandaru and Kalyanmoy Deb. 2015. Temporal innovization: Evolution of design principles using multi-objective optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) Vol. 9018. Springer Verlag 79--93.","DOI":"10.1007\/978-3-319-15934-8_6"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818108"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1778800"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.969114"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/S1052623496307510"},{"key":"e_1_2_2_8_1","volume-title":"Search Methodologies","author":"Deb Kalyanmoy","unstructured":"Kalyanmoy Deb and Kalyanmoy Deb . 2014. Multi-objective Optimization . In Search Methodologies . Springer US , Boston, MA , 403--449. Kalyanmoy Deb and Kalyanmoy Deb. 2014. Multi-objective Optimization. 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Mattson. 2003. The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization 25 2 (01 Jul 2003) 86--98.  A. Messac A. Ismail-Yahaya and C.A. Mattson. 2003. The normalized normal constraint method for generating the Pareto frontier. 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