Displaying similar documents to “Quadratic 0–1 programming: Tightening linear or quadratic convex reformulation by use of relaxations”

Redinv-SA: la simulated annealing for the quadratic assignment problem

N. M.M. de Abreu, T. M. Querido, P. O. Boaventura-Netto (2010)

RAIRO - Operations Research

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An algebraic and combinatorial approach to the study of the Quadratic Assignment Problem produced theoretical results that can be applied to (meta) heuristics to give them information about the problem structure, allowing the construction of algorithms. In this paper those results were applied to inform a Simulated Annealing-type heuristic (which we called RedInv-SA). Some results from tests with known literature instances are presented.

On semidefinite bounds for maximization of a non-convex quadratic objective over the unit ball

Mustafa Ç. Pinar, Marc Teboulle (2006)

RAIRO - Operations Research

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We consider the non-convex quadratic maximization problem subject to the unit ball constraint. The nature of the norm structure makes this problem extremely hard to analyze, and as a consequence, the same difficulties are encountered when trying to build suitable approximations for this problem by some tractable convex counterpart formulations. We explore some properties of this problem, derive SDP-like relaxations and raise open questions.

Branch-delete-bound algorithm for globally solving quadratically constrained quadratic programs

Zhisong Hou, Hongwei Jiao, Lei Cai, Chunyang Bai (2017)

Open Mathematics

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This paper presents a branch-delete-bound algorithm for effectively solving the global minimum of quadratically constrained quadratic programs problem, which may be nonconvex. By utilizing the characteristics of quadratic function, we construct a new linearizing method, so that the quadratically constrained quadratic programs problem can be converted into a linear relaxed programs problem. Moreover, the established linear relaxed programs problem is embedded within a branch-and-bound...