Computation of Large Invariant Subspaces Using Polynomial Filtered Lanczos Iterations with Applications in Density Functional Theory

The most expensive part of all electronic structure calculations based on density functional theory lies in the computation of an invariant subspace associated with some of the smallest eigenvalues of a discretized Hamiltonian operator. The dimension of this subspace typically depends on the total number of valence electrons in the system, and can easily reach hundreds or even thousands when large systems with many atoms are considered. At the same time, the discretization of Hamiltonians associated with large systems yields very large matrices, whether with planewave or real-space discretizations. The combination of these two factors results in one of the most significant bottlenecks in computational materials science. In this paper we show how to efficiently compute a large invariant subspace associated with the smallest eigenvalues of a symmetric/Hermitian matrix using polynomially filtered Lanczos iterations. The proposed method does not try to extract individual eigenvalues and eigenvectors. Instead, it constructs an orthogonal basis of the invariant subspace by combining two main ingredients. The first is a filtering technique to dampen the undesirable contribution of the largest eigenvalues at each matrix-vector product in the Lanczos algorithm. This technique employs a well-selected low pass filter polynomial, obtained via a conjugate residual-type algorithm in polynomial space. The second ingredient is the Lanczos algorithm with partial reorthogonalization. Experiments are reported to illustrate the efficiency of the proposed scheme compared to state-of-the-art implicitly restarted techniques.