Versão não definitiva do artigoThe purpose of this work is to present an algorithm to solve nonlinear constrained optimization
problems, using the filter method with the inexact restoration (IR) approach. In the IR approach two
independent phases are performed in each iteration—the feasibility and the optimality phases. The
first one directs the iterative process into the feasible region, i.e. finds one point with less constraints
violation. The optimality phase starts from this point and its goal is to optimize the objective function
into the satisfied constraints space. To evaluate the solution approximations in each iteration a scheme
based on the filter method is used in both phases of the algorithm. This method replaces the merit
functions that are based on penalty schemes, avoiding the related difficulties such as the penalty
parameter estimation and the non-differentiability of some of them. The filter method is implemented
in the context of the line search globalization technique. A set of more than two hundred AMPL test
problems is solved. The algorithm developed is compared with LOQO and NPSOL software packages.Fundação para a Ciência e a Tecnologia (FCT