Interior-point methods for nonconvex nonlinear programming: cubic regularization

In this paper, we present a primal-dual interior-point method for solving nonlinear programming problems. It employs a Levenberg-Marquardt (LM) perturbation to the Karush-Kuhn-Tucker (KKT) matrix to handle indefinite Hessians and a line search to obtain sufficient descent at each iteration. We show that the LM perturbation is equivalent to replacing the Newton step by a cubic regularization step with an appropriately chosen regularization parameter. This equivalence allows us to use the favorable theoretical results of Griewank (The modification of Newton’s method for unconstrained optimization by bounding cubic terms, <CitationRef CitationID="CR16">1981</CitationRef>), Nesterov and Polyak (Math. Program., Ser. A 108:177–205, <CitationRef CitationID="CR24">2006</CitationRef>), Cartis et al. (Math. Program., Ser. A 127:245–295, <CitationRef CitationID="CR5">2011</CitationRef>; Math. Program., Ser. A 130:295–319, <CitationRef CitationID="CR6">2011</CitationRef>), but its application at every iteration of the algorithm, as proposed by these papers, is computationally expensive. We propose a hybrid method: use a Newton direction with a line search on iterations with positive definite Hessians and a cubic step, found using a sufficiently large LM perturbation to guarantee a steplength of 1, otherwise. Numerical results are provided on a large library of problems to illustrate the robustness and efficiency of the proposed approach on both unconstrained and constrained problems. Copyright Springer Science+Business Media New York 2014