New LP-based local and global algorithms for continuous and mixed-integer nonconvex quadratic programming

In this work, we propose a new approach called “Successive Linear Programming Algorithm (SLPA)” for finding an approximate global minimizer of general nonconvex quadratic programs. This algorithm can be initialized by any extreme point of the convex polyhedron of the feasible domain. Furthermore, we...

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Vydané v:	Journal of global optimization Ročník 82; číslo 4; s. 659 - 689
Hlavní autori:	Bentobache, Mohand, Telli, Mohamed, Mokhtari, Abdelkader
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2022 Springer Springer Nature B.V
Predmet:	Algorithms Computer Science Linear programming Mathematics Mathematics and Statistics Mixed integer Operations Research/Decision Theory Optimization Quadratic programming Real Functions 90C20 Local minimizer Nonconvex quadratic programming Concave quadratic programming Extreme point 90C26 90C59 Successive linear programming Global minimizer Numerical experiments Approximate global minimizer Simplex algorithm
ISSN:	0925-5001, 1573-2916
On-line prístup:	Získať plný text
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