Sparse Projected-Gradient Method As a Linear-Scaling Low-Memory Alternative to Diagonalization in Self-Consistent Field Electronic Structure Calculations

详细信息    查看全文

• 作者：Ernesto G. Birgin ; J. M. Martı ; ́ ; nez ; Leandro Martı ; ́ ; nez ; Gerd B. Rocha
• 刊名：Journal of Chemical Theory and Computation
• 出版年：2013
• 出版时间：February 12, 2013
• 年：2013
• 卷：9
• 期：2
• 页码：1043-1051
• 全文大小：358K
• 年卷期：v.9,no.2(February 12, 2013)
• ISSN：1549-9626

文摘

Large-scale electronic structure calculations usually involve huge nonlinear eigenvalue problems. A method for solving these problems without employing expensive eigenvalue decompositions of the Fock matrix is presented in this work. The sparsity of the input and output matrices is preserved at every iteration, and the memory required by the algorithm scales linearly with the number of atoms of the system. The algorithm is based on a projected gradient iteration applied to the constraint fulfillment problem. The computer time required by the algorithm also scales approximately linearly with the number of atoms (or non-null elements of the matrices), and the algorithm is faster than standard implementations of modern eigenvalue decomposition methods for sparse matrices containing more than 50鈥?00 non-null elements. The new method reproduces the sequence of semiempirical SCF iterations obtained by standard eigenvalue decomposition algorithms to good precision.