TY - GEN
T1 - A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems
AU - Cano Lengua, Miguel Angel
AU - Quiroz, Erik Alex Papa
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/5/28
Y1 - 2020/5/28
N2 - The location problem is of great interest in order to establish different location demands in the state or private sector. The model of this problem is usually reduced to a mathematical optimization problem. In this paper we present a proximal method to solve location problems where the objective function is quasi-convex and non-differentiable. We prove that the iterations given by the method are well defined and under some assumptions on the objective function we prove the convergence of the method.
AB - The location problem is of great interest in order to establish different location demands in the state or private sector. The model of this problem is usually reduced to a mathematical optimization problem. In this paper we present a proximal method to solve location problems where the objective function is quasi-convex and non-differentiable. We prove that the iterations given by the method are well defined and under some assumptions on the objective function we prove the convergence of the method.
KW - Global convergence
KW - Location theory
KW - Proximal point method
KW - Quasiconvex function
UR - https://www.scopus.com/pages/publications/85092378386
U2 - 10.1145/3404716.3404735
DO - 10.1145/3404716.3404735
M3 - Conference contribution
AN - SCOPUS:85092378386
T3 - ACM International Conference Proceeding Series
SP - 98
EP - 104
BT - Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
T2 - 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
Y2 - 28 May 2020 through 30 May 2020
ER -