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Interior-Point Methods for Nonconvex Nonlinear Programming: Cubic Regularization

Hande Y. Benson(benson***at***drexel.edu)
David F. Shanno(shannod***at***comcast.net)

Abstract: In this paper, we present a barrier method for solving nonlinear programming problems. It employs a Levenberg-Marquardt 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 Levenberg-Marquardt 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 (1981), Nesterov and Polyak (2006), and Cartis et.al. (2011). 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.

Keywords: interior-point methods, nonlinear programming, cubic regularization, Newton's method

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation:

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Entry Submitted: 02/16/2012
Entry Accepted: 02/16/2012
Entry Last Modified: 02/16/2012

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