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GERAD seminar

Polyhedral approaches for mixed-integer convex optimization

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Feb 8, 2019   10:45 AM — 12:00 PM

Miles Lubin Research scientist, Algorithms & Optimization team, Google, United States

Mixed-integer convex optimization problems (MICPs) are problems that become convex when all integrality constraints are relaxed. I will present recent advances in solving these problems to global optimality by constructing polyhedral relaxations in a higher-dimensional space. This work develops significant new connections between MICP and modeling with symmetric and nonsymmetric convex cones, a discovery that influenced the development of MOSEK version 9 with their support for exponential and power cones. I will present our own implementation of an iterative outer approximation algorithm and a branch-and-cut variant in the open-source MICP solver Pajarito.

This is joint work with Russell Bent, Chris Coey, Juan Pablo Vielma and Emre Yamangil.


Free entrance.
Welcome to everyone!

Andrea Lodi organizer

Location

Room 4488
André-Aisenstadt Building
Université de Montréal Campus
2920, chemin de la Tour
Montréal QC H3T 1J4
Canada

Associated organization

Research Axes

Research applications