Tensor methods for large, sparse unconstrained optimization

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Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optimization, 1 (1991), pp. 293-315], who describe these methods for small to moderate size problems. This paper extends these methods to large, sparse unconstrained optimization problems. This requires an entirely new way of solving the tensor model that makes the methods suitable for solving large, sparse optimization problems efficiently. We present test results for sets of problems where the Hessian at the minimizer is nonsingular and where it is singular. These results show that tensor methods are significantly more efficient and more reliable than standard methods ... continued below

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31 p.

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Bouaricha, A. November 1996.

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Description

Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optimization, 1 (1991), pp. 293-315], who describe these methods for small to moderate size problems. This paper extends these methods to large, sparse unconstrained optimization problems. This requires an entirely new way of solving the tensor model that makes the methods suitable for solving large, sparse optimization problems efficiently. We present test results for sets of problems where the Hessian at the minimizer is nonsingular and where it is singular. These results show that tensor methods are significantly more efficient and more reliable than standard methods based on Newton`s method.

Physical Description

31 p.

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OSTI as DE97001016

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  • Other Information: PBD: [1996]

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  • Other: DE97001016
  • Report No.: MCS-P--452-0794
  • Grant Number: W-31109-ENG-38
  • DOI: 10.2172/409872 | External Link
  • Office of Scientific & Technical Information Report Number: 409872
  • Archival Resource Key: ark:/67531/metadc688763

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  • November 1996

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  • July 25, 2015, 2:20 a.m.

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  • Dec. 11, 2015, 3:10 p.m.

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Bouaricha, A. Tensor methods for large, sparse unconstrained optimization, report, November 1996; Illinois. (digital.library.unt.edu/ark:/67531/metadc688763/: accessed September 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.