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Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS "qn" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the 'Scilab' optimization documentation located at <http://www.scilab.org/content/download/250/1714/file/optimization_in_scilab.pdf>.

copied from cf-staging / r-n1qn1
Type Size Name Uploaded Downloads Labels
conda 93.0 kB | win-64/r-n1qn1-6.0.1_6-r36h5b3a9a7_0.tar.bz2  4 years and 9 months ago 1023 main cf202003
conda 92.8 kB | win-64/r-n1qn1-6.0.1_6-r35h5b3a9a7_0.tar.bz2  4 years and 9 months ago 1023 main cf202003
conda 97.1 kB | osx-64/r-n1qn1-6.0.1_6-r35h935cc70_0.tar.bz2  4 years and 9 months ago 329 main cf202003
conda 97.2 kB | osx-64/r-n1qn1-6.0.1_6-r36h935cc70_0.tar.bz2  4 years and 9 months ago 341 main cf202003
conda 95.0 kB | linux-64/r-n1qn1-6.0.1_6-r35h9cc4df9_0.tar.bz2  4 years and 9 months ago 2925 main cf202003
conda 95.2 kB | linux-64/r-n1qn1-6.0.1_6-r36h9cc4df9_0.tar.bz2  4 years and 9 months ago 2948 main cf202003

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