Primal or dual strong-duality in nonconvex optimization and a class of quasiconvex problems having zero duality gap

Fabián Flores-Bazán (), William Echegaray (), Fernando Flores-Bazán () and Eladio Ocaña ()
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Fabián Flores-Bazán: Universidad de Concepción
William Echegaray: Universidad Nacional de Ingeniería
Fernando Flores-Bazán: Universidad del Bío Bío
Eladio Ocaña: Universidad Nacional de Ingeniería

Journal of Global Optimization, 2017, vol. 69, issue 4, No 3, 823-845

Abstract: Abstract Primal or dual strong-duality (or min-sup, inf-max duality) in nonconvex optimization is revisited in view of recent literature on the subject, establishing, in particular, new characterizations for the second case. This gives rise to a new class of quasiconvex problems having zero duality gap or closedness of images of vector mappings associated to those problems. Such conditions are described for the classes of linear fractional functions and that of quadratic ones. In addition, some applications to nonconvex quadratic optimization problems under a single inequality or equality constraint, are presented, providing new results for the fulfillment of zero duality gap or dual strong-duality.

Keywords: Zero duality gap; Strong duality; Linear fractional programming; Quasiconvex programming; Quadratic programming; 90C20; 90C46; 49N10; 49N15; 52A10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10898-017-0542-9

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