A quadratically-convergent algorithm for general nonlinear programming problems

This paper presents an algorithm for solving nonlinearly constrained nonlinear programming problems. The algorithm reduces the original problem to a sequence of linearly-constrained minimization problems, for which efficient algorithms are available. A convergence theorem is given which states that if the process is started sufficiently close to a strict second-order Kuhn—Tucker point, then the sequence produced by the algorithm exists and convergesR-quadratically to that point.Work sponsored by the United States Army under Contract No. DA-31-124-ARO-D-462.