Global optimization for MINLPs with separable nonconvexities

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Global optimization for MINLPs with separable nonconvexities: convex relaxations
and perspective reformulations.
Claudia D´Ambrosio
(Joint work with Antonio Frangioni and Claudio Gentile)
In this talk we present a global optimization method for solving mixed integer nonlinear
programming problems with separable nonconvexities. The method is based on convex
relaxations and we make use of perspective reformulations to speed up their resolution.
Computational results confirm that the perspective reformulation outperforms the standard
approaches.