Automatic differentiation tools in optimization software.

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The authors discuss the role of automatic differentiation tools in optimization software. We emphasize issues that are important to large-scale optimization and that have proved useful in the installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of the gradient and Hessian matrix for partially separable functions and shows that the gradient and Hessian matrix can be computed with guaranteed bounds in time and memory requirements.

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13 pages

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More, J. J. January 15, 2001.

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Description

The authors discuss the role of automatic differentiation tools in optimization software. We emphasize issues that are important to large-scale optimization and that have proved useful in the installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of the gradient and Hessian matrix for partially separable functions and shows that the gradient and Hessian matrix can be computed with guaranteed bounds in time and memory requirements.

Physical Description

13 pages

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  • 3rd International Conference/Workshop on Automatic Differentiation: From Simulation to Optimization, Nice (FR), 06/19/2000--06/23/2000

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  • Report No.: ANL/MCS/CP-103808
  • Grant Number: W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 775260
  • Archival Resource Key: ark:/67531/metadc715677

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • January 15, 2001

Added to The UNT Digital Library

  • Sept. 29, 2015, 5:31 a.m.

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  • March 11, 2016, 1:33 p.m.

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More, J. J. Automatic differentiation tools in optimization software., article, January 15, 2001; Illinois. (digital.library.unt.edu/ark:/67531/metadc715677/: accessed October 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.