## sparseHessianFD: Numerical estimation of sparse Hessians using ACM TOMS Algorithm
636

Computes Hessian of a scalar-valued function, and returns it in sparse Matrix format.
The user must supply the objective function, the
gradient, and the row and column indices of the non-zero elements of the lower
triangle of the Hessian (i.e., the sparsity structure must be known in advance).
The algorithm exploits this sparsity, so Hessians can be computed quickly even
when the number of arguments to the objective functions is large. This package is
intended to be useful for numeric optimization (e.g., with the trustOptim package)
or in simulation (e.g., the sparseMVN package). The underlying algorithm is ACM TOMS
Algorithm 636, written by Coleman, Garbow and More (ACM Transactions on Mathematical
Software, 11:4, Dec. 1985).

Version: |
0.1.1 |

Depends: |
Rcpp (≥ 0.9.6), RcppEigen (≥ 0.3.1), Matrix, methods |

LinkingTo: |
Rcpp, RcppEigen |

Suggests: |
plyr |

Published: |
2013-11-06 |

Author: |
R interface code by Michael Braun
Original Fortran code by Thomas F. Coleman, Burton S. Garbow and
Jorge J. More. |

Maintainer: |
Michael Braun <braunm at smu.edu> |

License: |
file LICENSE |

URL: |
mbraun.cox.smu.edu |

NeedsCompilation: |
yes |

Citation: |
NA |

Materials: |
NA |

CRAN checks: |
sparseHessianFD results |

#### Downloads:

#### Reverse dependencies: