An Interior-Point Perspective on Sensitivity Analysis in Semidefinite Programming

E. Alper Yildirim

We study the asymptotic behavior of the interior-point bounds arising from the work of Yildirim and Todd on sensitivity analysis in semidefinite programming in comparison with the optimal partition bounds. For perturbations of the right-hand side vector and the cost matrix, we show that the interior-point bounds evaluated on the central path using the Monteiro-Zhang family of search directions converge to the symmetrized version of the optimal partition bounds under appropriate nondegeneracy assumptions, which can be weaker than the usual notion of nondegeneracy. Furthermore, the analysis does not assume strict complementarity as long as the central path converges to the analytic center in a relatively controlled manner. We also show that the same convergence results carry over to iterates lying in an appropriate (very narrow) central path neighborhood if the Nesterov-Todd direction is used to evaluate the interior-point bounds.

Technical Report No. 1289, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY 14853-3801.

Contact: yildirim@orie.cornell.edu


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