Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288359 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] 4OR [ISSN:] 1614-2411 [Volume:] 19 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 549-570
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
A linearization technique for binary quadratic programs (BQPs) that comprise linear constraints is presented. The technique, called “inductive linearization”, extends concepts for BQPs with particular equation constraints, that have been referred to as “compact linearization” before, to the general case. Quadratic terms may occur in the objective function, in the set of constraints, or in both. For several relevant applications, the linear programming relaxations obtained from applying the technique are proven to be at least as strong as the one obtained with a well-known classical linearization. It is also shown how to obtain an inductive linearization automatically. This might be used, e.g., by general-purpose mixed-integer programming solvers.
Subjects: 
Non-linear programming
Binary quadratic programming
Mixed-integer programming
Linearization
JEL: 
R01
C05
C09
C10
C11
C20
C30
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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