Compressed Sensing for the Fast Computation of Matrices: Application to Molecular Vibrations

Abstract

This article presents a new method to compute matrices from numerical simulations based on the ideas of sparse sampling and compressed sensing. The method is useful for problems where the determination of the entries of a matrix constitutes the computational bottleneck. We apply this new method to an important problem in computational chemistry: the determination of molecular vibrations from electronic structure calculations, where our results show that the overall scaling of the procedure can be improved in some cases. Moreover, our method provides a general framework for bootstrapping cheap low-accuracy calculations in order to reduce the required number of expensive high-accuracy calculations, resulting in a significant 3× speed-up in actual calculations.

Abstract

We present a method for efficiently computing matrices in numerical simulations based on the ideas of compressed sensing, and apply it to the determination of molecular vibrations from quantum calculations.