## sparseHessianFD: Numerical Estimation of Sparse Hessians

Computes Hessian of a scalar-valued function, and returns it in
sparse Matrix format, using ACM TOMS Algorithm 636. 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: |
0.2.0 |

Depends: |
R (≥ 3.1.2), Rcpp (≥ 0.11.3), Matrix (≥ 1.1.4), methods |

LinkingTo: |
Rcpp, RcppEigen (≥ 0.3.2.3) |

Suggests: |
testthat, numDeriv, knitr |

Published: |
2015-02-04 |

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: |
coxprofs.cox.smu.edu/braunm |

NeedsCompilation: |
yes |

SystemRequirements: |
C++11 |

Citation: |
sparseHessianFD citation info |

Materials: |
NEWS |

CRAN checks: |
sparseHessianFD results |

#### Downloads:

#### Reverse dependencies: