### Andrea Lodi (University of Bologna)

#### Indicator constraints in mixed-integer programming

*Joint work with Pietro Belotti and Amaya Nogales Gómez.*

*Wednesday 12 February 2014 at 16.15, JCMB 6206*

##### Abstract

Mixed Integer Linear Programming (MILP) models are commonly used to model
indicator constraints, which either hold or are relaxed depending on the value
of a binary variable. Classification problems with Ramp Loss functions are an
important application of such models. Mixed Integer Nonlinar Programming
(MINLP) models are usually dismissed because they cannot be solved as
efficiently. However, we show here that a subset of classification problems
can be solved much more efficiently by a MINLP model with nonconvex
constraints. This calls for a reconsideration of the modeling of these
indicator constraints.

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