Quadratically and Superlinearly Convergent Algorithms for the Solution of Inequality Constrained Minimization Problems 1

In this paper some Newton and quasi-Newton algorithms for the solution of inequality constrained minimization problems are considered. All the algorithms described produce sequences fx k g converging q-superlinearly to the solution. Furthermore , under mild assumptions, a q-quadratic convergence rate in x is also attained. Other features of these algorithms are that the solution of linear systems of equations only is required at each iteration and that the strict complementarity assumption is never invoked. First the superlinear or quadratic convergence rate of a Newton-like algorithm is proved. Then, a simpler version of this algorithm is studied and it is shown that it is superlinearly convergent. Finally, quasi-Newton versions of the previous algorithms are considered and, provided the sequence deened by the algorithms converges, a characterization of superlinear convergence extending the result of Boggs, Tolle and Wang is given.