New Algorithms in Computational Chemistry
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In this project in the Physical Chemistry Program, Hayden and                   Wells address basic mathematical and computational problems                     inherent in determining the structure of macromolecules from                    various experimental data, especially nuclear magnietc                          resonance spectroscopy.  Presently this category of nonlinear                   optimization problems is completely solvable only for small                     molecules.                                                                                                                                                      A new characterization of Euclidean distance matrices has                       provided new insight into these problems and provides the                       foundation for the development of an algorithm for the                          embedding problem of computational chemistry.  This algorithm,                  based on von Neumann's idea of alternating projections,                         promises a dramatic reduction in computational time for certain                 phases of the overall structure determination.  In numerous                     test cases the time reduction is from exponential order to                      polynomial growth of order four.
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[{'Code': '1144', 'Text': 'Molecular Biophysics'}, {'Code': '1271', 'Text': 'COMPUTATIONAL MATHEMATICS'}, {'Code': '1956', 'Text': 'STATISTICAL AND SIMULATIONS'}]
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