## Abstract

This paper presents an active-set algorithm for large-scale optimization that occupies the middle ground between sequential quadratic programming and sequential linear-quadratic programming methods. It consists of two phases. The algorithm first minimizes a piecewise linear approximation of the Lagrangian, subject to a linearization of the constraints, to determine a working set. Then, an equality constrained subproblem based on this working set and using second derivative information is solved in order to promote fast convergence. A study of the local and global convergence properties of the algorithm highlights the importance of the placement of the interpolation points that determine the piecewise linear model of the Lagrangian.

Original language | English (US) |
---|---|

Pages (from-to) | 289-324 |

Number of pages | 36 |

Journal | Mathematical Programming |

Volume | 137 |

Issue number | 1-2 |

DOIs | |

State | Published - Jan 1 2013 |

## Keywords

- 65K05
- 90C06
- 90C30
- 90C55
- Mathematics Subject Classification (2000): 49M37

## ASJC Scopus subject areas

- Mathematics(all)
- Software