Quadratically and superlinearly convergent algorithms for the solution of inequality constrained minimization problems

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 {x _k } convergingq-superlinearly to the solution. Furthermore, under mild assumptions, aq-quadratic convergence rate inx is also attained. Other features of these algorithms are that only the solution of linear systems of equations 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 defined by the algorithms converges, a characterization of superlinear convergence extending the result of Boggs, Tolle, and Wang is given.This research was supported by the National Research Program Metodi di Ottimizzazione per la Decisioni, MURST, Roma, Italy.