Math @ Duke
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Publications [#243730] of Jianfeng Lu
Papers Published
- Lai, R; Lu, J; Osher, S, Density matrix minimization with ℓ1 regularization,
Communications in Mathematical Sciences, vol. 13 no. 8
(January, 2015),
pp. 2097-2117, International Press of Boston, ISSN 1539-6746 [arXiv:1403.1525], [doi]
(last updated on 2024/09/17)
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 ℓ1 regularization. The minimization problem can be efficiently solved by a split Bregman 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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