A preprocessing technique for quadratic unconstrained binary optimization

To solve combinatorial optimization problems more easily, it can be valuable to identify a set of necessary or sufficient conditions that an optimal solution of the problem must satisfy. For instance, weak and strong duality conditions in linear programming support the development of the well-known...

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Veröffentlicht in:Discrete optimization Jg. 58; S. 100914
Hauptverfasser: Gueye, S., Michelon, P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.11.2025
Elsevier
Schlagworte:
Combinatorial optimization
Computer Science
Constraint programming
Karush–Kuhn–Tucker conditions
Operations Research
Persistencies
Quadratic unconstrained binary optimization
Combinatorial optimization
90Cxx
Karush–Kuhn–Tucker conditions
Constraint programming
Quadratic unconstrained binary optimization
Persistencies
ISSN:1572-5286, 1873-636X
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Beschreibung
Zusammenfassung:To solve combinatorial optimization problems more easily, it can be valuable to identify a set of necessary or sufficient conditions that an optimal solution of the problem must satisfy. For instance, weak and strong duality conditions in linear programming support the development of the well-known optimization algorithms for these problems. Similarly, the Karush–Kuhn–Tucker conditions give necessary and sufficient conditions for optimality in convex quadratic programming that underlie the development of optimization algorithms in this domain. Although such continuous conditions do not exist for integer programming, some necessary conditions can be derived from Karush–Kuhn–Tucker conditions for the Quadratic Unconstrained Binary Optimization (QUBO) problem. We present these conditions and show how they lead to a derivation of well-known criteria for fixing the values of single variables in the QUBO problem. From this, we show how to generalize these criteria to fix a product of any number of p (integer) literals, which also may be viewed as a generalization of the persistency notion, consisting of clauses of a Constraint Satisfaction Problem that the optimal solution must satisfy. We then couple our list of persistencies with state-of-the-art rules not covered by our approach. The resulting integrated set of conditions for fixing values of variables is tested in computational experiments on instances from standard databases available in the literature, showing that we can fix more variables and reduce problems more fully than previous approaches.
ISSN:1572-5286
1873-636X
DOI:10.1016/j.disopt.2025.100914