Remark on Algorithm 566

Victoria Z. Averbukh; Samuel Figueroa; Tamar Schlick

doi:10.1145/192115.192128

Remark on Algorithm 566

Victoria Z. Averbukh, Samuel Figueroa, Tamar Schlick

Chemistry

Research output: Contribution to journal › Article › peer-review

Abstract

We report the development of second-derivative FORTRAN routines to supplement Algorithm 566 developed by J. More´ et al. 1994. Algorithm 566 provides function and gradient subroutines of 18 test functions for multivariate minimization. Our supplementary Hessian segments enable users to test optimization software that requires second derivative information. Eigenvalue analysis throughout the minimization is now possible, with the goal of better understanding progress by different minimization algorithms and the relation of progress to eigenvalue distribution and condition number.

Original language	English (US)
Pages (from-to)	282-285
Number of pages	4
Journal	ACM Transactions on Mathematical Software (TOMS)
Volume	20
Issue number	3
DOIs	https://doi.org/10.1145/192115.192128
State	Published - Jan 9 1994

Keywords

Hessian subroutines

ASJC Scopus subject areas

Software
Applied Mathematics

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abstract = "We report the development of second-derivative FORTRAN routines to supplement Algorithm 566 developed by J. More´ et al. 1994. Algorithm 566 provides function and gradient subroutines of 18 test functions for multivariate minimization. Our supplementary Hessian segments enable users to test optimization software that requires second derivative information. Eigenvalue analysis throughout the minimization is now possible, with the goal of better understanding progress by different minimization algorithms and the relation of progress to eigenvalue distribution and condition number.",

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AU - Schlick, Tamar

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AB - We report the development of second-derivative FORTRAN routines to supplement Algorithm 566 developed by J. More´ et al. 1994. Algorithm 566 provides function and gradient subroutines of 18 test functions for multivariate minimization. Our supplementary Hessian segments enable users to test optimization software that requires second derivative information. Eigenvalue analysis throughout the minimization is now possible, with the goal of better understanding progress by different minimization algorithms and the relation of progress to eigenvalue distribution and condition number.

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Remark on Algorithm 566

Abstract

Keywords

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