A parallel approximation algorithm for mixed packing and covering semidefinite programs

We present a parallel approximation algorithm for a class of mixed packing and covering semidefinite programs which generalize on the class of positive semidefinite programs as considered by Jain and Yao [6]. As a corollary we get a faster approximation algorithm for positive semidefinite programs with better dependence of the parallel running time on the approximation factor, as compared to that of Jain and Yao [6]. Our algorithm and analysis is on similar lines as that of Young [10] who considered analogous linear programs.

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