Sharp Estimation of Convergence Rate for Self-Consistent Field Iteration to Solve Eigenvector-Dependent Nonlinear Eigenvalue Problems
Zhaojun Bai, Ren‐Cang Li, Ding Lu
Abstract
We present a comprehensive convergence analysis for the self-consistent field (SCF) iteration to solve a class of nonlinear eigenvalue problems with eigenvector dependency (NEPvs). Using the tangent-angle matrix as an intermediate measure for approximation error, we establish new formulas for two fundamental quantities that characterize the local convergence behavior of the plain SCF: the local contraction factor and the local asymptotic average contraction factor. In comparison with previously established results, new convergence rate estimates provide much sharper bounds on the convergence speed. As an application, we extend the convergence analysis to a popular SCF variant---the level-shifted SCF. The effectiveness of the convergence rate estimates is demonstrated numerically for NEPvs arising from solving the Kohn--Sham equation in electronic structure calculation and the Gross--Pitaevskii equation for modeling of the Bose--Einstein condensation.