## Abstract

Many real world discrete optimization problems are expressible as nested problems where we solve one optimization or satisfaction problem as a subproblem of a larger meta problem. Nested problems include many important problem classes such as: stochastic constraint satisfaction/ optimization, quantified constraint satisfaction/optimization and minimax problems. In this paper we define a new class of problems called nested constraint programs (NCP) which include the previously mentioned problem classes as special cases, and describe a search-based CP solver for solving NCP's.We briefly discuss how nogood learning can be used to significantly speedup such an NCP solver. We show that the new solver can be significantly faster than existing solvers for the special cases of stochastic/ quantified CSP/COP's, and that it can solve new types of problems which cannot be solved with existing solvers.

Original language | English |
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Title of host publication | Principles and Practice of Constraint Programming |

Subtitle of host publication | 20th International Conference, CP 2014 Lyon, France, September 8-12, 2014 Proceedings |

Editors | Barry O’Sullivan |

Place of Publication | Cham Switzerland |

Publisher | Springer |

Pages | 240-255 |

Number of pages | 16 |

ISBN (Electronic) | 9783319104287 |

ISBN (Print) | 9783319104270 |

DOIs | |

Publication status | Published - 2014 |

Externally published | Yes |

Event | International Conference on Principles and Practice of Constraint Programming 2014 - Lyon, France Duration: 8 Sep 2014 → 12 Sep 2014 Conference number: 20th http://cp2014.a4cp.org/ |

### Publication series

Name | Lecture Notes in Computer Science |
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Publisher | Springer |

Volume | 8656 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Conference

Conference | International Conference on Principles and Practice of Constraint Programming 2014 |
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Abbreviated title | CP 2014 |

Country | France |

City | Lyon |

Period | 8/09/14 → 12/09/14 |

Internet address |