Superlinear Convergence of Primal-Dual Interior Point Algorithms for NonlinearProgramming

摘要

The local convergence properties of a class of primal-dual interior point methodsare analyzed. These methods are designed to minimize a nonlinear, nonconvex, objective function subject to linear equality constraints and general inequalities. They involve an inner iteration in which the log-barrier merit function is approximately minimized subject to satisfying the linear equality constraints, and an outer iteration that specifies both the decrease in the barrier parameter and the level of accuracy for the inner minimization.