A feasible BFGS interior point algorithm for solving strongly convex minimization problems
Résumé
We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters $\mu$ converging to zero. We prove that it converges q-superlinearly for each fixed $\mu$ and that it is globally convergent when $\mu\to0$.
