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Exponential Data Fitting and its Applications
Real and complex exponential data fitting is an important activity in many different areas of science and engineering, ranging from Nuclear Magnetic Resonance Spectroscopy and Lattice Quantum Chromodynamics to Electrical and Chemical Engineering, Vision and Robotics. The most commonly used norm in t...
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Lenguaje: | eng |
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Bentham Science Publishers
2010
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/1438687 |
_version_ | 1780924637297770496 |
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author | Pereyra, Victor Scherer, Godela |
author_facet | Pereyra, Victor Scherer, Godela |
author_sort | Pereyra, Victor |
collection | CERN |
description | Real and complex exponential data fitting is an important activity in many different areas of science and engineering, ranging from Nuclear Magnetic Resonance Spectroscopy and Lattice Quantum Chromodynamics to Electrical and Chemical Engineering, Vision and Robotics. The most commonly used norm in the approximation by linear combinations of exponentials is the l2 norm (sum of squares of residuals), in which case one obtains a nonlinear separable least squares problem. A number of different methods have been proposed through the years to solve these types of problems and new applications appear |
id | cern-1438687 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2010 |
publisher | Bentham Science Publishers |
record_format | invenio |
spelling | cern-14386872021-04-22T00:29:07Zhttp://cds.cern.ch/record/1438687engPereyra, VictorScherer, GodelaExponential Data Fitting and its ApplicationsMathematical Physics and Mathematics Real and complex exponential data fitting is an important activity in many different areas of science and engineering, ranging from Nuclear Magnetic Resonance Spectroscopy and Lattice Quantum Chromodynamics to Electrical and Chemical Engineering, Vision and Robotics. The most commonly used norm in the approximation by linear combinations of exponentials is the l2 norm (sum of squares of residuals), in which case one obtains a nonlinear separable least squares problem. A number of different methods have been proposed through the years to solve these types of problems and new applications appearBentham Science Publishersoai:cds.cern.ch:14386872010 |
spellingShingle | Mathematical Physics and Mathematics Pereyra, Victor Scherer, Godela Exponential Data Fitting and its Applications |
title | Exponential Data Fitting and its Applications |
title_full | Exponential Data Fitting and its Applications |
title_fullStr | Exponential Data Fitting and its Applications |
title_full_unstemmed | Exponential Data Fitting and its Applications |
title_short | Exponential Data Fitting and its Applications |
title_sort | exponential data fitting and its applications |
topic | Mathematical Physics and Mathematics |
url | http://cds.cern.ch/record/1438687 |
work_keys_str_mv | AT pereyravictor exponentialdatafittinganditsapplications AT scherergodela exponentialdatafittinganditsapplications |