S2MPJ and CUTEst optimization problems for Matlab, Python and Julia
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866911951231123456 |
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| author | Gratton, Serge Toint, Philippe L. |
| author_facet | Gratton, Serge Toint, Philippe L. |
| contents | A new decoder for the SIF test problems of the CUTEst collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most \cutest\ problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | S2MPJ and CUTEst optimization problems for Matlab, Python and Julia Gratton, Serge Toint, Philippe L. Optimization and Control Performance 49N99, 65K05, 65Y20, 68N99, 90C30 G.1.6 A new decoder for the SIF test problems of the CUTEst collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most \cutest\ problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations. |
| title | S2MPJ and CUTEst optimization problems for Matlab, Python and Julia |
| topic | Optimization and Control Performance 49N99, 65K05, 65Y20, 68N99, 90C30 G.1.6 |
| url | https://arxiv.org/abs/2407.07812 |