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We present an eigenspectrum partitioning scheme without inversion for the recently described real-space electronic transport code, TRANSEC. The primary advantage of TRANSEC is its highly parallel algorithm, which enables studying conductance in large systems. The present scheme adds a new source of parallelization, significantly enhancing TRANSEC's parallel scalability, especially for systems with many electrons. In principle, partitioning could enable super-linear parallel speedup, as we demonstrate in calculations within TRANSEC. In practical cases, we report better than five-fold improvement in CPU time and similar improvements in wall time, compared to previously-published large calculations. Importantly, the suggested scheme is relatively simple to implement. It can be useful for general large Hermitian or weakly non-Hermitian eigenvalue problems, whenever relatively accurate inversion via direct or iterative linear solvers is impractical.

Solving the Kohn-Sham eigenvalue problem constitutes the most computationally expensive part in self-consistent density functional theory (DFT) calculations. In a previous paper, we have proposed a nonlinear Chebyshev-filtered subspace iteration method, which avoids computing explicit eigenvectors except at the first SCF iteration. The method may be viewed as an approach to solve the original nonlinear Kohn-Sham equation by a nonlinear subspace iteration technique, without emphasizing the intermediate linearized Kohn-Sham eigenvalue problem. It reaches self-consistency within a similar number of SCF iterations as eigensolver-based approaches. However, replacing the standard diagonalization at each SCF iteration by a Chebyshev subspace filtering step results in a significant speedup over methods based on standard diagonalization. Here, we discuss an approach for implementing this method in multi-processor, parallel environment. Numerical results are presented to show that the method enables to perform a class of highly challenging DFT calculations that were not feasible before.

The evolution of the magnetic moment in iron clusters containing 20 to 400 atoms is investigated using first-principles numerical calculations based on density-functional theory and real-space pseudopotentials. Three families of clusters are studied, characterized by the arrangement of atoms: icosahedral, body-centered cubic centered on an atom site, and body-centered cubic centered on the bridge between two neighboring atoms. We find an overall decrease of magnetic moment as the clusters grow in size towards the bulk limit. Clusters with faceted surfaces are predicted to have magnetic moment lower than other clusters with similar size. As a result, the magnetic moment is observed to decrease as function of size in a non-monotonic manner, which explains measurements performed at low temperatures., Comment: 3 figures, to appear in Phys. Rev. Lett

We discuss the close connection between eigenvalue computation and optimization using the Newton method and subspace methods. From the connection we derive a new class of Newton updates. The new update formulation is similar to the well-known Jacobi-Davidson method. This similarity leads to simplified versions of the Jacobi-Davidson method and the inverse iteration generalized Davidson (IIGD) method. We prove that the projection subspace augmented by the updating direction from each of these methods is able to include the Rayleigh quotient iteration (RQI) direction. Hence, the locally quadratic (cubic for normal matrices) convergence rate of the RQI method is retained and strengthened by the subspace methods. The theory is supported by extensive numerical results. Preconditioned formulations are also briefly discussed for large scale eigenvalue problems., Comment: 21 pages, 4 figures