Superlinear Convergence of Interior-Point Algorithms for Semidefinite Programming

被引:0
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作者
F. A. Potra
R. Sheng
机构
[1] University of Iowa,Department of Mathematics
[2] Argonne National Laboratory,Mathematics and Computer Science Division
关键词
Semidefinite programming; path-following algorithms; infeasible interior-point algorithms; polynomiality; superlinear convergence;
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学科分类号
摘要
We prove the superlinear convergence of the primal-dual infeasible interior-point path-following algorithm proposed recently by Kojima, Shida, and Shindoh and by the present authors, under two conditions: (i) the semidefinite programming problem has a strictly complementary solution; (ii) the size of the central path neighborhood approaches zero. The nondegeneracy condition suggested by Kojima, Shida, and Shindoh is not used in our analysis. Our result implies that the modified algorithm of Kojima, Shida, and Shindoh, which enforces condition (ii) by using additional corrector steps, has superlinear convergence under the standard assumption of strict complementarity. Finally, we point out that condition (ii) can be made weaker and show the superlinear convergence under the strict complementarity assumption and a weaker condition than (ii).
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页码:103 / 119
页数:16
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