A New Primal-Dual Interior-Point Algorithm for Semidefinite Optimization

Yong-Hoon Lee, Jin-Hee Jin, G. Cho
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Abstract

We propose a new primal-dual interior-point algorithm for semidefinite optimization(SDO) based on an eligible barrier function. New search directions and proximity measures are proposed based on the barrier function. We show that the algorithm has O(√n log(n/ε)) and O(√n(log n)log(n/ε)) complexity results for small- and large-update methods, respectively. These are the best known complexity results for such methods.
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半定优化的一种新的原对偶内点算法
提出了一种新的基于合适的障碍函数的半定优化(SDO)的原对偶内点算法。提出了基于障碍函数的新的搜索方向和接近度量。我们证明了该算法对于小更新和大更新方法分别具有O(√n log(n/ε))和O(√n(log n)log(n/ε))复杂度结果。这些是此类方法最著名的复杂性结果。
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