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Strong formulations for conic quadratic optimization with indicator variables

Andres Gomez (agomez***at***pitt.edu)

Abstract: We study the convex hull of the mixed-integer set given by a conic quadratic inequality and indicator variables. Conic quadratic terms are often used to encode uncertainties, while the indicator variables are used to model fixed costs or enforce sparsity in the solutions. We provide the convex hull description of the set under consideration when the continuous variables are unbounded. We propose valid nonlinear inequalities for the bounded case, and show that they describe the convex hull for the two-variable case. All the proposed inequalities are described in the original space of variables and are SOCP-representable. We present computational experiments demonstrating the strength of the proposed formulations.

Keywords: Conic quadratic optimization, mixed-integer optimization, submodularity, conic quadratic cuts

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation: Research report AG 18.02, IE, University of Pittsburgh, May 2018

Download: [PDF]

Entry Submitted: 05/09/2018
Entry Accepted: 05/09/2018
Entry Last Modified: 04/15/2020

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