On a general class of interior-point algorithms
for semidefinite programming with polynomial complexity
and superlinear convergence
Rongqin Sheng, Florian A. Potra and Jun Ji
We propose a unified analysis for a class of infeasible-start
predictor-corrector algorithms for semidefinite programming problems, using
the Monteiro-Zhang unified direction. The algorithms are direct generalizations
of the Mizuno-Todd-Ye predictor-corrector algorithm for linear programming.
We show that the algorithms belonging to this class are globally convergent,
provided the problem has a solution, and have optimal computational
complexity. We also give simple sufficient conditions for superlinear
convergence. Our results generalize the results obtained by the first two
authors for the
infeasible-interior-point algorithm proposed by Kojima, Shida and Shindoh
and Potra and Sheng.
Reports on Computational Mathematics, No. 89/1996, Department of
Mathematics, The University of Iowa
Contact: [email protected]
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