A Global Convergence Analysis of an Algorithm for Large-Scale Nonlinear Optimization Problems

In this paper we give a global convergence analysis of a basic version of an SQP algorithm described in 2] for the solution of large scale nonlinear inequality-constrained optimization problems. Several procedures and options have been added to the basic algorithm to improve the practical performance; some of these are also analyzed. The important features of the algorithm include the use of a constrained merit function to assess the progress of the iterates and a sequence of approximate merit functions that are less expensive to evaluate. It also employs an interior point quadratic programming solver that can be terminated early to produce a truncated step.