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# 1. Introduction

Linear-scaling electronic structure methods [1] are essential for calculations of large systems containing many atoms. One of the criteria for the success of such methods is the use of a high quality localized basis set, which is usually non-orthogonal. Using such a basis set, one can formulate the electronic structure problem as a generalized eigenvalue problem [2,3,4], which also arises naturally in many other scientific disciplines. The properties of and are that they are Hermitian matrices and that is also positive definite. For the case where only the lowest few eigenvalue-eigenvector pairs of large matrices are required, most direct diagonalization (e.g. Cholesky-Householder procedure) methods which use similarity transformations [2,3,4,5] are inefficient because all eigenvalue-eigenvector pairs are found. The computational effort scales as , where is the number of basis functions in the calculation. Iterative methods which concentrate on only the lowest few eigenvalue-eigenvector pairs are much more efficient [6,7,8,9,10,11], and are widely used to solve the standard symmetric eigenvalue problem. Iterative solution of the generalized eigenvalue problem usually proceeds by first performing a Cholesky decomposition of to obtain a standard symmetric eigenvalue problem. However, in this work the generalized problem, cast into variational form, is solved by the conjugate gradient method without first transforming to symmetric form. The gradient method was proposed long ago by Hestenes and Karush [12,13] to solve the eigenvalue problem, where they used a steepest descent method to perform the minimization.

In this work, we first present an example of a generalized eigenvalue problem taken from first-principles electronic structure calculations. An iterative conjugate gradient minimization method which finds the lowest few eigenvalues and eigenvectors is then introduced. Although this method can be used for Hermitian and Hermitian-positive-definite , it will be most efficient when and are also sparse, a case which arises when large systems are studied with localized basis sets. We have taken the tensor nature of the search direction and other quantities into account. A preconditioning scheme related to that discussed by Bowler and Gillan in a previous Communication [14] to improve the convergence is proposed. To test the method we use a localized spherical-wave basis set introduced in another Communication [15] to perform first-principles calculations. Test cases are taken from the molecular chlorine and bulk silicon systems. The rate of convergence of the solutions is one of our main concerns here. Linear convergence of the solution is observed from the results of calculations.

Next: 2. Formulation of the Up: Preconditioned conjugate gradient method Previous: Preconditioned conjugate gradient method
Peter Haynes