TY - JOUR
T1 - QPLIB
T2 - a library of quadratic programming instances
AU - Furini, Fabio
AU - Traversi, Emiliano
AU - Belotti, Pietro
AU - Frangioni, Antonio
AU - Gleixner, Ambros
AU - Gould, Nick
AU - Liberti, Leo
AU - Lodi, Andrea
AU - Misener, Ruth
AU - Mittelmann, Hans
AU - Sahinidis, Nikolaos V.
AU - Vigerske, Stefan
AU - Wiegele, Angelika
N1 - Funding Information:
Acknowledgements We are grateful to all the donors who provided instances for the library. We gratefully acknowledge the financial support of the Gaspard Monge Program for Optimization and operations research (PGMO) and the logistic support of GAMS for having provided us with a license for their software. Finally, we would like to acknowledge the financial and networking support by the COST Action TD1207. The work of the fifth and twelfth author was supported by the Research Campus MODAL Mathematical Optimization and Data Analysis Laboratories funded by the Federal Ministry of Education and Research (BMBF Grant 05M14ZAM). The work of the sixth author was supported by the EPSRC grant EP/M025179/1. All responsibility for the content of this publication is assumed by the authors.
Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature and The Mathematical Programming Society.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - Binary quadratic programming
KW - Instance library
KW - Mixed-Integer Quadratically Constrained Quadratic Programming
KW - Quadratic programming
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U2 - 10.1007/s12532-018-0147-4
DO - 10.1007/s12532-018-0147-4
M3 - Article
AN - SCOPUS:85065230079
VL - 11
SP - 237
EP - 265
JO - Mathematical Programming Computation
JF - Mathematical Programming Computation
SN - 1867-2949
IS - 2
ER -