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arXiv · 1403.1525

Density matrix minimization with $\ell_1$ regularization

Rongjie Lai·Jianfeng Lu·Stanley Osher
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Abstract

We propose a convex variational principle to find sparse representation of low-lying eigenspace of symmetric matrices. In the context of electronic structure calculation, this corresponds to a sparse density matrix minimization algorithm with $\ell_1$ regularization. The minimization problem can be efficiently solved by a split Bergman iteration type algorithm. We further prove that from any initial condition, the algorithm converges to a minimizer of the variational principle.

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Rongjie Lai, Jianfeng Lu, Stanley Osher. 2014-03-06. Density matrix minimization with $\ell_1$ regularization. https://arxiv.org/abs/1403.1525

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