A Primal-Dual Regularized Interior-Point Method for Convex Quadratic Programs


BibTeX reference

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.

, 31 pages

This cahier was revised in July 2011

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A primal-dual regularized interior-point method for convex quadratic programs
Mathematical Programming Computation, 4(1), 71–107, 2012 BibTeX reference