QPLIB: A Library of Quadratic Programming Instances

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, the constrains, or both are quadratic. QP is a very ``varied'' class of problems, comprising sub-classes of problems ranging from trivial to undecidable. Solution methods for QP are very diverse, ranging from entirely combinatorial ones to completely continuous ones, including many 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 many different interested communities is therefore important. We propose a simple taxonomy for QP instances that leads 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


@techreport{FuriniEtAl2017TR, author = "Fabio Furini, Emiliano Traversi, Pietro Belotti, Antonio Frangioni, Ambros Gleixner, Nick Gould, Leo Liberti, Andrea Lodi, Ruth Misener, Hans Mittelmann, Nick Sahinidis, Stefan Vigerske, and Angelika Wiegele", title = "{QPLIB}: {A} Library of Quadratic Programming Instances", year = "2017", month = "February", note = "Available at Optimization Online", url = "http://optimization-online.org/DB_HTML/2017/02/5846.html" }



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