Real Space DFT by Locally Optimal Block Preconditioned Conjugate Gradient Method

Real space approaches solve the Kohn-Sham (KS) DFT problem as a system of partial differential equations (PDE) in real space numerical grids. In such techniques, the Hamiltonian matrix is typically much larger but sparser than the matrix arising in state-of-the-art DFT codes which are often based on directly minimizing the total energy functional. Evidence of good performance of real space methods - by Chebyshev filtered subspace iteration (CFSI) - was reported by Zhou, Saad, Tiago and Chelikowsky [1]. We found that the performance of the locally optimal block preconditioned conjugate gradient method (LOGPCG) introduced by Knyazev [2], when used in conjunction with CFSI, generally exceeds that of CFSI for solving the KS equations. We will present our implementation of the LOGPCG based real space electronic structure calculator. \\[4pt] [1] Y. Zhou, Y. Saad, M. L. Tiago, and J. R. Chelikowsky, ``Self-consistent-field calculations using Chebyshev-filtered subspace iteration,'' J. Comput. Phys., vol. 219,pp. 172-184, November 2006. \\[0pt] [2] A. V. Knyazev, ``Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient method,'' SIAM J. Sci. Comput, vol. 23, pp. 517-541, 2001.