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QPLIB: a library of quadratic programming instances

Furini, Fabio; Traversi, Emiliano; Belotti, Pietro; Frangioni, Antonio; Gleixner, Ambros; Gould, Nick; Liberti, Leo; Lodi, Andrea; Misener, Ruth; Mittelmann, Hans; Sahinidis, Nikolaos V.; Vigerske, Stefan; Wiegele, Angelika (2019), QPLIB: a library of quadratic programming instances, Mathematical Programming Computation, 11, 2, p. 237-265. 10.1007/s12532-018-0147-4

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Type
Article accepté pour publication ou publié
Date
2019
Journal name
Mathematical Programming Computation
Volume
11
Number
2
Pages
237-265
Publication identifier
10.1007/s12532-018-0147-4
Metadata
Show full item record
Author(s)
Furini, Fabio
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Traversi, Emiliano
Laboratoire d'Informatique de Paris-Nord [LIPN]
Belotti, Pietro
autre
Frangioni, Antonio
Dipartimento di Informatica [Pisa]
Gleixner, Ambros
Optimization Department [ZIB]
Gould, Nick
autre
Liberti, Leo cc
Laboratoire d'informatique de l'École polytechnique [Palaiseau] [LIX]
Lodi, Andrea
autre
Misener, Ruth
Department of Computing
Mittelmann, Hans
School of Mathematical and Statistical Sciences
Sahinidis, Nikolaos V.
Department of Chemical Engineering
Vigerske, Stefan
autre
Wiegele, Angelika
Institut für Mathematik, Alpen-Adria-Universität Klagenfurt, Austria
Abstract (EN)
This paper describes a new instance library for quadratic programming (QP), i.e., the family of continuous and (mixed)-integer optimization problems where the objective function and/or the constraints are quadratic. QP is a very diverse class of problems, comprising sub-classes ranging from trivial to undecidable. This diversity is reflected in the variety of QP solution methods, ranging from entirely combinatorial approaches to completely continuous algorithms, including many methods for which both aspects are fundamental. Selecting a set of instances of QP that is at the same time not overwhelmingly onerous but sufficiently challenging for the different, interested communities is therefore important. We propose a simple taxonomy for QP instances leading to a systematic problem selection mechanism. We then briefly survey the field of QP, giving an overview of theory, methods and solvers. Finally, we describe how the library was put together, and detail its final contents.
Subjects / Keywords
Instance library; Quadratic programming; Mixed-Integer Quadratically Constrained Quadratic Programming; Binary quadratic programming

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