NUMERICAL MONTE CARLO AND EXACT DIAGONALIZATION RESULTS

Matthew Clifford Wilson

Naomichi Hatano, Masuo Suzuki

Malvin H. Kalos, G.A. Mikhailov

X. Zotos

Dean Lee

Dean Lee

We discuss a method called quasi-sparse eigenvector diagonalization which finds the most important basis vectors of the low energy eigenstates of a quantum Hamiltonian. It can operate using any basis, either orthogonal or non-orthogonal, and any sparse Hamiltonian, either Hermitian, non-Hermitian, finite-dimensional, or infinite-dimensional. The method is part of a new computational approach which combines both diagonalization and Monte Carlo techniques.