A simple iterative algorithm for generating selected eigenspaces of large matrices

Abstract

We propose a new iterative algorithm for generating a subset of eigenvalues and eigenvectors of large matrices which generalizes the method of optimal relaxations. We also give convergence criteria for the iterative process, investigate its efficiency by evaluating computer storage and time requirements and by a few numerical tests.

7 pages, 1 figure

References in corpus (1)

• The Anderson model of localization: a challenge for modern eigenvalue methods

Cited by in corpus (5)

• Importance Truncation for Large-Scale Configuration Interaction Approaches
• An importance sampling algorithm for generating exact eigenstates of the nuclear Hamiltonian
• Quantum localization and bound state formation in Bose-Einstein condensates
• Approximating the ground state of fermion system by multiple determinant states: matching pursuit approach
• Modified conjugated gradient method for diagonalising large matrices