Properties of the Augmented Lagrangian in Nonlinear Semidefinite Optimization - Sun, J.; Zhang, L. W.; Wu, Y. (Journal of optimization theory and applications, 2006)
We study the properties of the augmented Lagrangian function for nonlinear semidenite programming. It is shown that, under a set of sufcient conditions, the augmented Lagrangian algorithm is locally convergent when the penalty parameter is larger than a certain threshold. An error estimate of the solution, depending on the penalty parameter, is also established. [PUBLICATION ABSTRACT]