TY - JOUR

T1 - Characterizing quasiconvexity of the pointwise infimum of a family of arbitrary translations of quasiconvex functions, with applications to sums and quasiconvex optimization

AU - Flores-Bazán, F.

AU - García, Y.

AU - Hadjisavvas, N.

N1 - Publisher Copyright:
© 2021, Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society.
Funding Information:
The research for the first author was supported in part by ANID-Chile through FONDECYT 1181316 and PIA/BASAL AFB170001.
Funding Information:
Part of the work of the third author was carried out when he was visiting the Department of Mathematical Engineering, Universidad de Concepci?n (Chile). This author wishes to thank the Department for its hospitality. The authors want to thank both referees for their constructive criticism which lead to the present version of the manuscript.

PY - 2021/9

Y1 - 2021/9

N2 - It is well-known that the sum of two quasiconvex functions is not quasiconvex in general, and the same occurs with the minimum. Although apparently these two statements (for the sum or minimum) have nothing in common, they are related, as we show in this paper. To develop our study, the notion of quasiconvex family is introduced, and we establish various characterizations of such a concept: one of them being the quasiconvexity of the pointwise infimum of arbitrary translations of quasiconvex functions in the family; another is the convexity of the union of any two of their sublevel sets; a third one is the quasiconvexity of the sum of the quasiconvex functions, composed with arbitrary nondecreasing functions. As a by-product, any of the aforementioned characterizations, besides providing quasiconvexity of the sum, also implies the semistrict quasiconvexity of the sum if every function in the family has the same property. Three concrete applications in quasiconvex optimization are presented: First, we establish the convexity of the (Benson) proper efficient solution set to a quasiconvex vector optimization problem; second, we derive conditions that allow us to reduce a constrained optimization problem to one with a single inequality constraint, and finally, we show a class of quasiconvex minimization problems having zero duality gap.

AB - It is well-known that the sum of two quasiconvex functions is not quasiconvex in general, and the same occurs with the minimum. Although apparently these two statements (for the sum or minimum) have nothing in common, they are related, as we show in this paper. To develop our study, the notion of quasiconvex family is introduced, and we establish various characterizations of such a concept: one of them being the quasiconvexity of the pointwise infimum of arbitrary translations of quasiconvex functions in the family; another is the convexity of the union of any two of their sublevel sets; a third one is the quasiconvexity of the sum of the quasiconvex functions, composed with arbitrary nondecreasing functions. As a by-product, any of the aforementioned characterizations, besides providing quasiconvexity of the sum, also implies the semistrict quasiconvexity of the sum if every function in the family has the same property. Three concrete applications in quasiconvex optimization are presented: First, we establish the convexity of the (Benson) proper efficient solution set to a quasiconvex vector optimization problem; second, we derive conditions that allow us to reduce a constrained optimization problem to one with a single inequality constraint, and finally, we show a class of quasiconvex minimization problems having zero duality gap.

KW - Quasiconvex function

KW - Quasiconvex optimization

KW - Quasimonotone operator

UR - http://www.scopus.com/inward/record.url?scp=85103424460&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/ab5ffa55-f154-3221-b535-56d886b21cde/

U2 - 10.1007/s10107-021-01647-w

DO - 10.1007/s10107-021-01647-w

M3 - Article in a journal

AN - SCOPUS:85103424460

SN - 0025-5610

VL - 189

SP - 315

EP - 337

JO - Mathematical Programming

JF - Mathematical Programming

IS - 1-2

ER -