Abstract
We introduce the concept of sparse stochastic compression, an efficient stochastic sampling of any general function. The technique uses sparse stochastic orbitals (SSOs), short vectors that sample a small number of space points. As a first demonstration, SSOs are applied in conjunction with simple direct projection to accelerate our recent stochastic technique; the new developments enable accurate prediction of quasiparticle energies and gaps for systems with up to electrons, with small statistical errors of and using less than 2000 core CPU hours. Overall, stochastic scales now linearly (and often sublinearly) with
- Received 26 May 2018
- Revised 18 July 2018
DOI:https://doi.org/10.1103/PhysRevB.98.075107
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