Mathematical Programming: Series A and B
Local Convergence of the Affine-Scaling Interior-Point Algorithm for Nonlinear Programming
Computational Optimization and Applications
SIAM Journal on Optimization
Local behavior of the Newton method on two equivalent systems from linear programming
Journal of Optimization Theory and Applications
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In this paper, we compare the behavior of two Newton interior-point methods derived from two different first-order necessary conditions for the same nonlinear optimization problem with simple bounds. One set of conditions was proposed by Coleman and Li; the other is the standard KKT set of conditions. We discuss a perturbation of the CL conditions for problems with one-sided bounds and the difficulties involved in extending this to problems with general bounds. We study the numerical behavior of the Newton method applied to the systems of equations associated with the unperturbed and perturbed necessary conditions. Preliminary numerical results for convex quadratic objective functions indicate that, for this class of problems, the Newton method based on the perturbed KKT formulation appears to be the more robust.