- A multiplier method with a class of penalty functions for convex programming Romulo A Castillo(romulo.castilloufsc.br) Luiz C Matioli(matioliufpr.br) Clavel Quintana(clcarlonegmail.com) Abstract: We consider a class of augmented Lagrangian methods for solving convex programming problems with inequality constraints. This class involves a family of penalty functions and specific values of parameters $p,q,\tilde y \in R$ and $c>0$. The penalty family includes the classical modified barrier and the exponential function. The associated proximal method for solving the dual problem is also considered. Convergence results are shown, specifically we prove that any limit point of the primal and the dual sequence generated by the algorithms are optimal solutions of the primal and dual problem respectively Keywords: Multiplier methods, proximal point methods, convex programming. Category 1: Nonlinear Optimization Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization ) Citation: Tecnical report CNPq Prosul 490045/2010-3 Universidade federal de Paraná Brazil.2015 Download: [PDF]Entry Submitted: 01/20/2016Entry Accepted: 01/20/2016Entry Last Modified: 01/20/2016Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.