Projective Block Lanczos Algorithm for Dense, Hermitian Eigensystems

Abstract

Projection operators are used to effect “deflation by restriction” and it is argued that this is an optimal Lanczos algorithm for memory minimization. Algorithmic optimization is constrained to dense, Hermitian eigensystems where a significant number of the extreme eigenvectors must be obtained reliably and completely. The defining constraints are operator algebra without a matrix representation and semi-orthogonalization without storage of Krylov vectors. Other semi-orthogonalization strategies for Lanczos algorithms and conjugate gradient techniques are evaluated within these constraints. Large scale, sparse, complex numerical experiments are performed on clusters of magnetic dipoles, a quantum many-body system that is not block-diagonalizable. Plane-wave, density functional theory of beryllium clusters provides examples of dense complex eigensystems. Use of preconditioners and spectral transformations is evaluated in a preprocessor prior to a high accuracy self-consistent field calculation.