Computation of Two-Dimensional Spectra Assisted by Compressed Sampling.

The computation of scientific data can be very time-consuming, even if they are ultimately determined by a small number of parameters. The principle of compressed sampling suggests that for typical data we can achieve a considerable decrease in the computation time by avoiding the need to sample the full data set. We demonstrate the usefulness of this approach at the hand of two-dimensional (2-D) spectra in the context of ultrafast nonlinear spectroscopy of biological systems where numerical calculations are highly challenging due to the considerable computational effort involved in obtaining individual data points.

[1]  Urs Sennhauser,et al.  Atomically flat single-crystalline gold nanostructures for plasmonic nanocircuitry. , 2010, Nature communications.

[2]  C. Kreisbeck,et al.  Modelling of oscillations in two-dimensional echo-spectra of the Fenna–Matthews–Olson complex , 2011, 1110.1511.

[3]  S. Huelga,et al.  The nature of the low energy band of the Fenna-Matthews-Olson complex: vibronic signatures. , 2011, The Journal of chemical physics.

[4]  Justin R Caram,et al.  Long-lived quantum coherence in photosynthetic complexes at physiological temperature , 2010, Proceedings of the National Academy of Sciences.

[5]  X. Andrade,et al.  Application of compressed sensing to the simulation of atomic systems , 2012, Proceedings of the National Academy of Sciences.

[6]  Shiqian Ma,et al.  Fixed point and Bregman iterative methods for matrix rank minimization , 2009, Math. Program..

[7]  Javier Prior,et al.  Efficient simulation of strong system-environment interactions. , 2010, Physical review letters.

[8]  Emmanuel J. Candès,et al.  Matrix Completion With Noise , 2009, Proceedings of the IEEE.

[9]  D. Gross,et al.  Efficient quantum state tomography. , 2010, Nature communications.

[10]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..

[11]  Semion K. Saikin,et al.  Probing Biological Light-Harvesting Phenomena by Optical Cavities , 2011, 1110.1386.

[12]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[13]  T. Mančal,et al.  Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems , 2007, Nature.

[14]  David Gross,et al.  Recovering Low-Rank Matrices From Few Coefficients in Any Basis , 2009, IEEE Transactions on Information Theory.

[15]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[16]  D. Tronrud,et al.  The structural basis for the difference in absorbance spectra for the FMO antenna protein from various green sulfur bacteria , 2009, Photosynthesis Research.

[17]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..

[18]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[19]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[20]  Xavier Andrade,et al.  Compressed Sensing for Multidimensional Spectroscopy Experiments. , 2012, The journal of physical chemistry letters.

[21]  Justin R. Caram,et al.  Dynamics of electronic dephasing in the Fenna–Matthews–Olson complex , 2010 .