Nukala, Phani

Title: Low-rank updates in statistical physics applications
Author: Phani Nukala
Affiliation: Oak Ridge National Laboratory, USA
Abstract:
This paper presents efficient low-rank updating algorithms for a variety of materials science and statistical physics applications. In particular, we present efficient algorithms for (1) simulating fracture of disordered materials, (2) modeling colossal magnetoresistance effect in manganites using spin-fermion models, (3) describing strongly correlated systems using the Hubbard model, and (4) describing many-body problems using Quantum Monte Carlo (QMC). These algorithms are based on low-rank updating of underlying linear algebra problem and result in significant computational savings often in the range of three to ten times faster than competing algorithms.
References:
1) P.K.V.V. Nukala et al, Fast Update Algorithm for the Quantum Monte Carlo Simulation of the Hubbard Model, Phys. Rev. B (in print)
2) P.K.V.V. Nukala and P. Kent, A Fast Algorithm for Slater Determinant Updates in Quantum Monte Carlo Simulations, Journal of Chemical Physics 130(20), 204105 (2009).
3) G. Alvarez, P. K. V. V. Nukala, and E. D'Azevedo, Fast diagonalization of evolving matrices: Application to Spin-Fermion Models, J. Stat. Mech.: Theory and Experiment P08007 (2007).