Title: Mixed-integer convex optimization
Speaker: Miles Lubin
Affiliation: Massachusetts Institute of Technology
Location: Room 217 Huxley Building
Time: 3:00pm

Abstract. Mixed-integer convex optimization problems are convex problems with the additional (non-convex) constraints that some variables may take only integer values. Despite the past decades’ advances in algorithms and technology for both mixed-integer *linear* and *continuous, convex* optimization, mixed-integer convex optimization problems have remained relatively more challenging and less widely used in practice. In this talk, we describe our recent algorithmic work on mixed-integer convex optimization which has yielded advances over the state of the art, including the globally optimal solution of open benchmark problems. Based on our developments, we have released Pajarito, an open-source solver written in Julia and accessible from popular optimization modeling frameworks. Pajarito is immediately useful for solving challenging mixed combinatorial continuous problems arising from engineering and statistical applications.

About the speaker. Miles Lubin is a Ph.D. candidate in Operations Research at the Massachusetts Institute of Technology advised by Juan Pablo Vielma. His research interests span diverse areas of mathematical optimization, with a unifying theme of developing new methodologies for large-scale optimization drawing from motivating applications in renewable energy. He has published work in chance constrained optimization, mixed-integer conic optimization, robust optimization, stochastic programming, algebraic modeling, automatic differentiation, numerical linear algebra, and parallel computing techniques for large-scale problems. He is an author of the JuMP modeling package and co-founder of the JuliaOpt organization for optimization software written in Julia.