Displaying similar documents to “Branch-delete-bound algorithm for globally solving quadratically constrained quadratic programs”

Nonmonotone strategy for minimization of quadratics with simple constraints

M. A. Diniz-Ehrhardt, Zdeněk Dostál, M. A. Gomes-Ruggiero, J. M. Martínez, Sandra Augusta Santos (2001)

Applications of Mathematics

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An algorithm for quadratic minimization with simple bounds is introduced, combining, as many well-known methods do, active set strategies and projection steps. The novelty is that here the criterion for acceptance of a projected trial point is weaker than the usual ones, which are based on monotone decrease of the objective function. It is proved that convergence follows as in the monotone case. Numerical experiments with bound-constrained quadratic problems from CUTE collection show...

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.

Quadratic 0–1 programming: Tightening linear or quadratic convex reformulation by use of relaxations

Alain Billionnet, Sourour Elloumi, Marie-Christine Plateau (2008)

RAIRO - Operations Research

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Many combinatorial optimization problems can be formulated as the minimization of a 0–1 quadratic function subject to linear constraints. In this paper, we are interested in the exact solution of this problem through a two-phase general scheme. The first phase consists in reformulating the initial problem either into a compact mixed integer linear program or into a 0–1 quadratic convex program. The second phase simply consists in submitting the reformulated problem to a standard solver....

Minimization of a convex quadratic function subject to separable conical constraints in granular dynamics

Pospíšil, Lukáš, Dostál, Zdeněk

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The numerical solution of granular dynamics problems with Coulomb friction leads to the problem of minimizing a convex quadratic function with semidefinite Hessian subject to a separable conical constraints. In this paper, we are interested in the numerical solution of this problem. We suggest a modification of an active-set optimal quadratic programming algorithm. The number of projection steps is decreased by using a projected Barzilai-Borwein method. In the numerical experiment, we...