In a recent paper [Int. J. Quant. Chem. (2016) DOI: 10.1002/qua.25144,
arXiv:1502.06579] Markovich, Blau, Sanders, and Aspuru-Guzik presented a
numerical evaluation and comparison of three methods, Compressed Sensing (CS),
Super-Resolution (SR), and Filter Diagonalization (FDM), on their ability of
"recovering information" from time signals, concluding that CS and RS
outperform FDM. We argue that this comparison is invalid for the following
reasons. FDM is a well established method designed for solving the harmonic
inversion problem or, similarly, for the problem of spectral estimation, and as
such should be applied only to problems of this kind. The authors incorrectly
assume that the problem of data fitting is equivalent to the spectral
estimation problem, regardless of what parametric form is used, and,
consequently, in all five numerical examples FDM is applied to the wrong
problem. Moreover, the authors' implementation of FDM turned out to be
incorrect, leading to extremely bad results, caused by numerical instabilities.
As we demonstrate here, if implemented correctly, FDM could still be used for
fitting the data, at least for the time signals composed of damped sinusoids,
resulting in superior performance. In addition, we show that the published
article is full of inaccuracies, mistakes and incorrect statements.