5 citations · 6 across the 8 of their papers we have counts for
8 papers
A Linear Scalarization Proximal Point Method for Quasiconvex Multiobjective Minimization
Erik Alex Papa Quiroz, Hellena Christina Fernandes Apolinário, Kely Diana Villacorta Villacorta +1
In this paper we propose a linear scalarization proximal point algorithm for solving arbitrary lower semicontinuous quasiconvex multiobjective minimization problems. Under some nat…
A Scalarization Proximal Point Method for Quasiconvex Multiobjective Minimization
H. C. F. Apolinário, E. A. Papa Quiroz, P. R. Oliveira
In this paper we propose a scalarization proximal point method to solve multiobjective unconstrained minimization problems with locally Lipschitz and quasiconvex vector functions.…
The self regulation problem as an inexact steepest descent method for multicriteria optimization
G. C. Bento, J. X. Cruz Neto, P. R. Oliveira +1
In this paper, we study an inexact steepest descent method, with Armijo's rule, for multicriteria optimization. The sequence generated by the method is guaranteed to be well-define…
Convergence of the Gauss-Newton method for a special class of systems of equations under a majorant condition
Max L. N. Gonçalves, Paulo R. Oliveira
In this paper, we study the Gauss-Newton method for a special class of systems of nonlinear equation. Under the hypothesis that the derivative of the function under consideration s…
Local convergence analysis of inexact Gauss-Newton like methods under majorant condition
O. P. Ferreira, M. L. N. Goncalves, P. R. Oliveira
In this paper, we present a local convergence analysis of inexact Gauss-Newton like methods for solving nonlinear least squares problems. Under the hypothesis that the derivative o…
Local convergence analysis of Gauss-Newton's method under majorant condition
O. P. Ferreira, M. L. N. Goncalves, P. R. Oliveira
The Gauss-Newton's method for solving nonlinear least squares problems is studied in this paper. Under the hypothesis that the derivative of the function associated with the least…