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    <id>8894</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>432</pageFirst>
    <pageLast>455</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>27</volume>
    <type>article</type>
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    <publisherPlace/>
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    <title language="eng">An Algorithm-independent Measure of Progress for Linear Constraint Propagation</title>
    <parentTitle language="eng">Constraints</parentTitle>
    <identifier type="doi">10.1007/s10601-022-09338-9</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2022-09-26</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Boro Šofranac</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Ambros Gleixner</author>
    <author>Sebastian Pokutta</author>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
    <collection role="persons" number="sofranac">Šofranac, Boro</collection>
    <collection role="institutes" number="Mathematical Algorithmic Intelligence">Mathematical Algorithmic Intelligence</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="projects" number="Math+EF1-9">Math+EF1-9</collection>
  </doc>
  <doc>
    <id>8960</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>114</pageFirst>
    <pageLast>123</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>13884</volume>
    <type>conferenceobject</type>
    <publisherName>Springer, Cham</publisherName>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <title language="eng">Online Learning for Scheduling MIP Heuristics</title>
    <abstract language="eng">Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often solve large real-world problems within minutes. This success can partially be attributed to heuristics. Since their behavior is highly instance-dependent, relying on hard-coded rules derived from empirical testing on a large heterogeneous corpora of benchmark instances might lead to sub-optimal performance. In this work, we propose an online learning approach that adapts the application of heuristics towards the single instance at hand. We replace the commonly used static heuristic handling with an adaptive framework exploiting past observations about the heuristic’s behavior to make future decisions. In particular, we model the problem of controlling Large Neighborhood Search and Diving – two broad and complex classes of heuristics – as a multi-armed bandit problem. Going beyond existing work in the literature, we control two different classes of heuristics simultaneously by a single learning agent. We verify our approach numerically and show consistent node reductions over the MIPLIB 2017 Benchmark set. For harder instances that take at least 1000 seconds to solve, we observe a speedup of 4%.</abstract>
    <parentTitle language="eng">Integration of Constraint Programming, Artificial Intelligence, and Operations Research</parentTitle>
    <identifier type="doi">10.1007/978-3-031-33271-5_8</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-89142</enrichment>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <author>Antonia Chmiela</author>
    <submitter>Antonia Chmiela</submitter>
    <author>Ambros Gleixner</author>
    <author>Pawel Lichocki</author>
    <author>Sebastian Pokutta</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="projects" number="Math+EF1-9">Math+EF1-9</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>9271</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Scylla: a matrix-free fix-propagate-and-project heuristic for mixed-integer optimization</title>
    <parentTitle language="eng">Proceedings of Conference of the Society for Operations Research in Germany</parentTitle>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Gioni Mexi</author>
    <submitter> Spiegel</submitter>
    <author>Mathieu Besançon</author>
    <author>Suresh Bolusani</author>
    <author>Antonia Chmiela</author>
    <author>Alexander Hoen</author>
    <author>Ambros Gleixner</author>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="mexi">Mexi, Gioni</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="hoen">Hoen, Alexander</collection>
    <collection role="projects" number="Math+EF1-9">Math+EF1-9</collection>
  </doc>
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