Solving procedures for quadratic programming with optional equality and inequality constraints, which can be used for by sequential quadratic programming (SQP). Similar to Newton-Raphson methods in the unconstrained case, sequential quadratic programming solves non-linear constrained optimization problems by iteratively solving linear approximations of the optimality conditions of such a problem (cf. Powell (1978) <doi:10.1007/BFb0067703>; Nocedal and Wright (1999, ISBN: 978-0-387-98793-4)). The Hessian matrix in this strategy is commonly approximated by the BFGS method in its damped modification proposed by Powell (1978) <doi:10.1007/BFb0067703>. All methods are implemented in C++ as header-only library, such that it is easy to use in other packages.

Version: | 0.5 |

Imports: | Rcpp (≥ 1.0.0), Matrix, Rdpack |

LinkingTo: | Rcpp, RcppArmadillo, RcppEigen |

Published: | 2020-03-31 |

Author: | Simon Lenau |

Maintainer: | Simon Lenau <lenau at uni-trier.de> |

License: | GPL-3 |

NeedsCompilation: | yes |

SystemRequirements: | C++11, GNU Make |

CRAN checks: | sqp results |

Reference manual: | sqp.pdf |

Package source: | sqp_0.5.tar.gz |

Windows binaries: | r-devel: sqp_0.5.zip, r-release: sqp_0.5.zip, r-oldrel: sqp_0.5.zip |

macOS binaries: | r-release: sqp_0.5.tgz, r-oldrel: sqp_0.5.tgz |

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