{"624232":{"#nid":"624232","#data":{"type":"event","title":"ISyE Department Seminar - Juan Pablo Vielma","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMore than 50 years of development have made mixed integer linear\u003Cbr \/\u003E\r\nprogramming (MILP) an extremely successful tool. MILP\u0026rsquo;s modeling\u003Cbr \/\u003E\r\nflexibility allows it describe a wide range of business, engineering\u003Cbr \/\u003E\r\nand scientific problems, and, while MILP is NP-hard, many of these\u003Cbr \/\u003E\r\nproblems are routinely solved in practice thanks to state-of-the-art\u003Cbr \/\u003E\r\nsolvers that nearly double their machine-independent speeds every\u003Cbr \/\u003E\r\nyear. Inspired by this success, the last decade has seen a surge of\u003Cbr \/\u003E\r\nactivity on the solution and application of mixed integer convex\u003Cbr \/\u003E\r\nprogramming (MICP), which extends MILP\u0026rsquo;s versatility by allowing the\u003Cbr \/\u003E\r\nuse of convex constraints in addition to linear inequalities. In this\u003Cbr \/\u003E\r\ntalk we cover various recent developments concerning theory,\u003Cbr \/\u003E\r\nalgorithms and computation for MICP. Solvers for MICP can be\u003Cbr \/\u003E\r\nsignificantly more effective than those for more general non-convex\u003Cbr \/\u003E\r\noptimization, so one of the questions we cover in this talk is what\u003Cbr \/\u003E\r\nclasses of non-convex constraints can be modeled through MICP. We also\u003Cbr \/\u003E\r\ncover various topics concerning the modeling and computational\u003Cbr \/\u003E\r\nsolution of MICP problems using the Julia programming language and the\u003Cbr \/\u003E\r\nJuMP modeling language for optimization. In particular, we show how\u003Cbr \/\u003E\r\nmixed integer optimal control problems where the variables are\u003Cbr \/\u003E\r\npolynomials can be easily modeled and solved by seamlessly combining\u003Cbr \/\u003E\r\nseveral Julia packages and JuMP extensions with the Julia-written MICP\u003Cbr \/\u003E\r\nsolver Pajarito.jl. Finally, we introduce the Julia-based interior\u003Cbr \/\u003E\r\npoint solver for general non-symmetric cones Hypatia.jl, and show how\u003Cbr \/\u003E\r\nits use of non-standard cones can allow it to outperform commercial\u003Cbr \/\u003E\r\nsolvers in certain instances.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EBio\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nJuan Pablo Vielma is the Richard S. Leghorn (1939) Career Development\u003Cbr \/\u003E\r\nAssociate Professor at MIT Sloan School of Management and is\u003Cbr \/\u003E\r\naffiliated to MIT\u0026rsquo;s Operations Research Center. Dr. Vielma has a B.S.\u003Cbr \/\u003E\r\nin Mathematical Engineering from University of Chile and a Ph.D. in\u003Cbr \/\u003E\r\nIndustrial Engineering from the Georgia Institute of Technology. His\u003Cbr \/\u003E\r\ncurrent research interests include the theory and practice of\u003Cbr \/\u003E\r\nmixed-integer mathematical optimization and applications in energy,\u003Cbr \/\u003E\r\nnatural resource management, marketing and statistics. In January of\u003Cbr \/\u003E\r\n2017 he was named by President Obama as one of the recipients of the\u003Cbr \/\u003E\r\nPresidential Early Career Award for Scientists and Engineers (PECASE).\u003Cbr \/\u003E\r\nSome of his other recognitions include the NSF CAREER Award and the\u003Cbr \/\u003E\r\nINFORMS Computing Society Prize. He is currently an associate editor\u003Cbr \/\u003E\r\nfor Operations Research and Operations Research Letters, a member of\u003Cbr \/\u003E\r\nthe board of directors of the INFORMS Computing Society, and a member\u003Cbr \/\u003E\r\nof the NumFocus steering committee for JuMP.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMore than 50 years of development have made mixed integer linear\u003Cbr \/\u003E\r\nprogramming (MILP) an extremely successful tool. MILP\u0026rsquo;s modeling\u003Cbr \/\u003E\r\nflexibility allows it describe a wide range of business, engineering\u003Cbr \/\u003E\r\nand scientific problems, and, while MILP is NP-hard, many of these\u003Cbr \/\u003E\r\nproblems are routinely solved in practice thanks to state-of-the-art\u003Cbr \/\u003E\r\nsolvers that nearly double their machine-independent speeds every\u003Cbr \/\u003E\r\nyear. Inspired by this success, the last decade has seen a surge of\u003Cbr \/\u003E\r\nactivity on the solution and application of mixed integer convex\u003Cbr \/\u003E\r\nprogramming (MICP), which extends MILP\u0026rsquo;s versatility by allowing the\u003Cbr \/\u003E\r\nuse of convex constraints in addition to linear inequalities. In this\u003Cbr \/\u003E\r\ntalk we cover various recent developments concerning theory,\u003Cbr \/\u003E\r\nalgorithms and computation for MICP. Solvers for MICP can be\u003Cbr \/\u003E\r\nsignificantly more effective than those for more general non-convex\u003Cbr \/\u003E\r\noptimization, so one of the questions we cover in this talk is what\u003Cbr \/\u003E\r\nclasses of non-convex constraints can be modeled through MICP. We also\u003Cbr \/\u003E\r\ncover various topics concerning the modeling and computational\u003Cbr \/\u003E\r\nsolution of MICP problems using the Julia programming language and the\u003Cbr \/\u003E\r\nJuMP modeling language for optimization. In particular, we show how\u003Cbr \/\u003E\r\nmixed integer optimal control problems where the variables are\u003Cbr \/\u003E\r\npolynomials can be easily modeled and solved by seamlessly combining\u003Cbr \/\u003E\r\nseveral Julia packages and JuMP extensions with the Julia-written MICP\u003Cbr \/\u003E\r\nsolver Pajarito.jl. Finally, we introduce the Julia-based interior\u003Cbr \/\u003E\r\npoint solver for general non-symmetric cones Hypatia.jl, and show how\u003Cbr \/\u003E\r\nits use of non-standard cones can allow it to outperform commercial\u003Cbr \/\u003E\r\nsolvers in certain instances.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Modeling Power of Mixed Integer Convex Optimization Problems And Their Effective Solution with Julia and JuMP"}],"uid":"34868","created_gmt":"2019-08-09 13:30:56","changed_gmt":"2019-09-11 13:40:53","author":"sbryantturner3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-09-11T14:30:00-04:00","event_time_end":"2019-09-11T15:30:00-04:00","event_time_end_last":"2019-09-11T15:30:00-04:00","gmt_time_start":"2019-09-11 18:30:00","gmt_time_end":"2019-09-11 19:30:00","gmt_time_end_last":"2019-09-11 19:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}