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  <doc>
    <id>1166</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation>National ICT Australia, University of Melbourne</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2010-03-18</completedDate>
    <publishedDate>2010-03-18</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Rapid Learning for Binary Programs</title>
    <abstract language="eng">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.</abstract>
    <identifier type="serial">10-04</identifier>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="opus3-id">1227</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-11663</identifier>
    <enrichment key="SourceTitle">App. in: CPAIOR 2010, Proceedings, Andrea Lodi et al. (eds.) Springer 2010, LNCS 6140, pp. 51-55</enrichment>
    <author>Timo Berthold</author>
    <submitter>unknown unknown</submitter>
    <author>Thibaut Feydy</author>
    <author>Peter Stuckey</author>
    <series>
      <title>ZIB-Report</title>
      <number>10-04</number>
    </series>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Ganzzahlige Programmierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Constraintprogrammierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Primalheuristik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Konfliktanalyse</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>constraint programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>primal heuristic</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>conflict learning</value>
    </subject>
    <collection role="ddc" number="510">Mathematik</collection>
    <collection role="ccs" number="G.1.6">Optimization</collection>
    <collection role="ccs" number="I.2.3">Deduction and Theorem Proving (F.4.1)</collection>
    <collection role="msc" number="68Q32">Computational learning theory [See also 68T05]</collection>
    <collection role="msc" number="90C09">Boolean programming</collection>
    <collection role="msc" number="90C59">Approximation methods and heuristics</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1166/ZR_10_04.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/1166/ZR_10_04.ps</file>
  </doc>
</export-example>
