Rapid Learning for Binary Programs
Please always quote using this URN:urn:nbn:de:0296-matheon-9726
- Learning during search allows solvers for discrete optimization problems to remember parts of the search that they have already performed and avoid revisiting redundant parts. Learning approaches pioneered by the SAT and CP communities have been successfully incorporated into the SCIP constraint integer programming platform. In this paper we show that performing a heuristic constraint programming search during root node processing of a binary program can rapidly learn useful nogoods, bound changes, primal solutions, and branching statistics that improve the remaining IP search.
Author: | Timo Berthold, Thibaut Feydy, Peter Stuckey |
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URN: | urn:nbn:de:0296-matheon-9726 |
Referee: | Martin Grötschel |
Document Type: | Preprint, Research Center Matheon |
Language: | English |
Date of first Publication: | 2012/01/15 |
Release Date: | 2012/01/15 |
Tag: | binary programming; conflict learning; constraint programming; primal heuristic |
Institute: | Zuse Institute Berlin (ZIB) |
MSC-Classification: | 68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section {04 in that areag 68-00 General reference works (handbooks, dictionaries, bibliographies, etc.) / 68Qxx Theory of computing / 68Q32 Computational learning theory [See also 68T05] |
90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING / 90Cxx Mathematical programming [See also 49Mxx, 65Kxx] / 90C09 Boolean programming | |
Preprint Number: | 853 |