# A primal-dual regularized interior-point method for convex quadratic programs

Mathematical Programming Computation, 4(1):71–107, 2012

Mathematical Programming Computation, 4(1):71–107, 2012

Interior-point methods in augmented form for linear and convex quadratic programming require the solution of a sequence of symmetric indefinite linear systems which are used to derive search directions. Safeguards are typically required in order to handle free variables or rank-deficient Jacobians. We propose a consistent framework and accompanying theoretical justification for regularizing these linear systems. Our approach can be interpreted as a simultaneous proximal-point regularization of the primal and dual problems. The regularization is termed *exact* to emphasize that, although the problems are regularized, the algorithm recovers a solution of the original problem, for appropriate values of the regularization parameters.

The code for the interior algorithm is implemented as part of the NLPy optimization package.