Some numerical experience with a globally convergent algorithm for nonlinearly constrained optimization

Global convergence properties are established for a quite general form of algorithms for solving nonlinearly constrained minimization problems. A useful feature of the methods considered is that they can be implemented easily either with or without using quadratic programming techniques. A particular implementation, designed to be both efficient and robust, is described in detail. Numerical results are presented and discussed.This work was carried out by the first author under the support of the Science Research Council (UK), Grant Nos. B/RG/95124 and GR/A/44480, which are gratefully acknowledged.