Rapid Learning for Binary Programs

Please always quote using this URN: urn:nbn:de:0297-zib-11663

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.

Metadaten
Author:	Timo Berthold ORCiD, Thibaut Feydy, Peter Stuckey
Document Type:	ZIB-Report
Tag:	Constraintprogrammierung; Ganzzahlige Programmierung; Konfliktanalyse; Primalheuristik conflict learning; constraint programming; integer programming; primal heuristic
MSC-Classification:	68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area) / 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
	90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING / 90Cxx Mathematical programming [See also 49Mxx, 65Kxx] / 90C59 Approximation methods and heuristics
CCS-Classification:	G. Mathematics of Computing / G.1 NUMERICAL ANALYSIS / G.1.6 Optimization
	I. Computing Methodologies / I.2 ARTIFICIAL INTELLIGENCE / I.2.3 Deduction and Theorem Proving (F.4.1)
Contributing Corporation:	National ICT Australia, University of Melbourne
Date of first Publication:	2010/03/18
Series (Serial Number):	ZIB-Report (10-04)
ISSN:	1438-0064
ZIB-Reportnumber:	10-04
Published in:	App. in: CPAIOR 2010, Proceedings, Andrea Lodi et al. (eds.) Springer 2010, LNCS 6140, pp. 51-55

Open Access