Exploiting Sparsity in Semidefinite Programming via Matrix Completion I: General Framework

  title={Exploiting Sparsity in Semidefinite Programming via Matrix Completion I: General Framework},
  author={Mituhiro Fukuda and Masakazu Kojima and Kazuo Murota and Kazuhide Nakata},
  journal={SIAM Journal on Optimization},
A critical disadvantage of primal-dual interior-point methods compared to dual interior-point methods for large scale semidefinite programs (SDPs) has been that the primal positive semidefinite matrix variable becomes fully dense in general even when all data matrices are sparse. Based on some fundamental results about positive semidefinite matrix completion, this article proposes a general method of exploiting the aggregate sparsity pattern over all data matrices to overcome this disadvantage… CONTINUE READING
Highly Influential
This paper has highly influenced 23 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 240 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 157 extracted citations

241 Citations

Citations per Year
Semantic Scholar estimates that this publication has 241 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 35 references

\ Exploiting sparsity in primaldual interiorpoint methods for semide nite programming

  • M. Kojima
  • \ Numerical evaluation of SDPA ( SemiDe nite…
  • 1999

Similar Papers

Loading similar papers…