Superlinear Convergence of Primal-Dual Interior Point Algorithms for Nonlinear Programming
Superlinear Convergence of Primal-Dual Interior Point Algorithms for Nonlinear Programming
The local convergence properties of a class of primal-dual interior point methods are 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 …