In this paper we present a hybrid path-following algorithm that generates inexact Newton steps suited for solving large scale and/or degenerate nonlinear programs. The algorithm uses as a central region a relaxed notion of the central path, called quasicentral path, a generalized augmented Lagrangian function, weighted proximity measures, and a linesearch within a trust region strategy. We apply a semi-iterative method for obtaining inexact Newton steps by using the conjugate gradient algorithm as an iterative procedure. We present a numerical comparison, and some promising results are reported.
Key words: Interior-point methods, trust region methods, linesearch technique, nonlinear programming, and conjugate gradient.
En este artículo nosotros presentamos un algoritmo híbrido de seguimiento de camino que genera pasos inexactos de Newton para resolver problemas de gran escala o degenerados para programación no lineal. El algoritmo usa como una region de centralidad una noción mas débil que el bien conocido camino central, llamada camino quasi-central, una generalización de la función aumentada de Lagrange, medidas de aproximación pesadas, y una dirección de búsqueda dentro de una region de verdad. Nosotros aplicamos un método semi-iterativo para obtener direcciones inexactas del método de Newton usando el algoritmo del gradiente conjugado y presentamos una comparación numérica con resultados prometedores.
Palabras clave: Métodos de punto interior, Métodos de región verdadera, técnica de búsqueda de línea, programación nolinear y gradiente conjugado.
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