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Computational experience with methods for estimating sparse hessians for nonlinear optimization

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Abstract

The paper compares a factorized sparse quasi-Newton update of Goldfarb with a nonfactorized BFGS sparse update of Shanno on a series of test problems, with numerical results strongly favoring the unfactorized update. Analysis of Goldfarb's method is done to explain the poor numerical performance. Two specific conjugate gradient methods for solving the required systems of linear equations with the unfactorized update are described and tested.

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Authors and Affiliations

  1. Department of Management-Information Systems, College of Business and Public Administration, University of Arizona, Tuscon, Arizona

    D. F. Shanno (Professor)

Authors
  1. D. F. Shanno
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Additional information

Communicated by H. Y. Huang

This research was supported by the National Science Foundation under Grant No. MCS-77-07327

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Shanno, D.F. Computational experience with methods for estimating sparse hessians for nonlinear optimization. J Optim Theory Appl 35, 183–193 (1981). https://doi.org/10.1007/BF00934575

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  • DOI: https://doi.org/10.1007/BF00934575

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