1. Reducing memory cost of exact diagonalization using singular value decomposition.
- Author
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Weinstein, Marvin, Auerbach, Assa, and Chandra, V. Ravi
- Subjects
- *
ALGORITHMS , *LANCZOS method , *LATTICE theory , *HAMILTONIAN systems , *OSCILLATIONS - Abstract
We present a modified Lanczos algorithm to diagonalize lattice Hamiltonians with dramatically reduced memory requirements, without restricting to variational ansatzes. The lattice of size N is partitioned into two subclusters. At each iteration the Lanczos vector is projected into two sets of nsvd smaller subcluster vectors using singular value decomposition. For low entanglement entropy See, (satisfied by short-range Hamiltonians), the truncation error is expected to vanish as exp(Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.). Convergence is tested for the Heisenberg model on Kagomé clusters of 24, 30, and 36 sites, with no lattice symmetries exploited, using less than 15 GB of dynamical memory. Generalization of the Lanczos-SVD algorithm to multiple partitioning is discussed, and comparisons to other techniques are given. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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