  Solving semidefinite programs (SDP) in a short time is the key to managing
various mathematical optimization problems. The matrix-completion primal-dual
interior-point method (MC-PDIPM) extracts a sparse structure of input SDP by
factorizing the variable matrices. In this paper, we propose a new
factorization based on the inverse of the variable matrix to enhance the
performance of MC-PDIPM. We also use multithreaded parallel computing to deal
with the major bottlenecks in MC-PDIPM. Numerical results show that the new
factorization and multithreaded computing reduce the computation time for SDPs
that have structural sparsity.
