A sequential semismooth Newton method for the nearest low-rank correlation matrix problem

Qingna Li*, Hou Duo Qi

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

32 引用 (Scopus)

摘要

Based on the well-known result that the sum of the largest eigenvalues of a symmetric matrix can be represented as a semidefinite programming problem (SDP), we formulate the nearest low-rank correlation matrix problem as a nonconvex SDP and propose a numerical method that solves a sequence of least-square problems. Each of the least-square problems can be solved by a specifically designed semismooth Newton method, which is shown to be quadratically convergent. The sequential method is guaranteed to produce a stationary point of the nonconvex SDP. Our numerical results demonstrate the high efficiency of the proposed method on large scale problems.

源语言英语
页(从-至)1641-1666
页数26
期刊SIAM Journal on Optimization
21
4
DOI
出版状态已出版 - 2011
已对外发布

指纹

探究 'A sequential semismooth Newton method for the nearest low-rank correlation matrix problem' 的科研主题。它们共同构成独一无二的指纹。

引用此