FDIPA - algoritmo de pontos interiores e direções viáveis para otimização não-linear diferenciável: um estudo de parâmetros

Abstract

This work presents a study on the influence of the parameters of an interior point and feasible directions algorithm for solving non-linear problems. The algorithm, named FDIPA, aims to find among the points of a set defined by equality and/or inequality constraints, those which minimize a differentiable function. The FDIPA is based on two linear systems with the same coefficient matrix, obtained from the Karush-Kuhn-Tucker first order necessary conditions. From a initial point in the interior of the feasible set, FDIPA generates a sequence of points which are also interior to the set. At each iteration, FDIPA produces a descent direction which is deflected towards the interior of the feasible set in order to create a new descent and feasible direction. Then, a linear search is performed to get a new interior point and assure the global convergence of the method. A family of algorithms can be obtained varying the rules used to update the parameters of the FDIPA. The study presented here has been done considering just one particular algorithm and inequality constraints only. Numerical tests pointed to a certain choice of parameters which led to a fewer number of iterations when solving some test problems.