Approximation of rank function and its application to the nearest low-rank correlation matrix
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Publication:386463
DOI10.1007/s10898-012-0007-0zbMath1307.90141OpenAlexW2000139644MaRDI QIDQ386463
Le Han, Shujun Bi, Shaohua Pan
Publication date: 9 December 2013
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-012-0007-0
approximationconvex relaxationsemismooth Newton methodnearest low-rank correlation matrixrank optimization problem
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First order optimality conditions for mathematical programs with semidefinite cone complementarity constraints, A new approximation of the matrix rank function and its application to matrix rank minimization
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Cites Work
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