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  <doc>
    <id>5771</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Methoden zur Reduktion der Rechenzeit linearer Optimierungsmodelle in der Energiewirtschaft - Eine Performance-Analyse</title>
    <abstract language="deu">Dieser Beitrag stellt mögliche Ansätze zur Reduktion der Rechenzeit von linearen Optimierungsproblemen mit energiewirtschaftlichem Anwendungshintergrund vor. Diese Ansätze bilden im Allgemeinen die Grundlage für konzeptionelle Strategien zur Beschleunigung von Energiesystemmodellen. Zu den einfachsten Beschleunigungsstrategien zählt die Verkleinerung der Modelldimensionen, was beispielsweise durch Ändern der zeitlichen, räumlichen oder technologischen Auflösung eines Energiesystemmodells erreicht werden kann. Diese Strategien sind zwar häufig ein Teil der Methodik in der Energiesystemanalyse, systematische Benchmarks zur Bewertung ihrer Effektivität werden jedoch meist nicht durchgeführt. Die vorliegende Arbeit adressiert genau diesen Sachverhalt. Hierzu werden Modellinstanzen des Modells REMix in verschiedenen Größenordnungen mittels einer Performance-Benchmark-Analyse untersucht. Die Ergebnisse legen zum einen den Schluss nahe, dass verkürzte Betrachtungszeiträume das größte Potential unter den hier analysierten Strategien zur Reduktion von Rechenzeit bieten. Zum anderen empfiehlt sich die Verwendung des Barrier-Lösungsverfahrens mit multiplen Threads unter Vernachlässigung des Cross-Over.</abstract>
    <parentTitle language="deu">EnInnov 2016: 14. Symposium Energieinnovation 2016</parentTitle>
    <enrichment key="FulltextUrl">http://www.tugraz.at/fileadmin/user_upload/Events/Eninnov2016/files/lf/Session_C2/LF_Cao.pdf</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Karl-Kiên Cao</author>
    <submitter>Matthias Miltenberger</submitter>
    <author>Ambros Gleixner</author>
    <author>Matthias Miltenberger</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="BEAM-ME">BEAM-ME</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>7113</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Algorithmic Behavior for Solving Mixed Integer Programs Using Bandit Algorithms</title>
    <abstract language="eng">State-of-the-art solvers for mixed integer programs (MIP) govern a variety of algorithmic components. Ideally, the solver adaptively learns to concentrate its computational budget on those components that perform well on a particular problem, especially if they are time consuming. We focus on three such algorithms, namely the classes of large neighborhood search and diving heuristics as well as Simplex pricing strategies. For each class we propose a selection strategy that is updated based on the observed runtime behavior, aiming to ultimately select only the best algorithms for a given instance. We review several common strategies for such a selection scenario under uncertainty, also known as Multi Armed Bandit Problem. In order to apply those bandit strategies, we carefully design reward functions to rank and compare each individual heuristic or pricing algorithm within its respective class. Finally, we discuss the computational benefits of using the proposed adaptive selection within the SCIP Optimization Suite on publicly available MIP instances.</abstract>
    <parentTitle language="eng">OR 2018: International Conference on Operations Research</parentTitle>
    <enrichment key="Series">Operations Research 2018 Proceedings</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">accepted for publication</enrichment>
    <enrichment key="AcceptedDate">2018-11-16</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-69563</enrichment>
    <author>Gregor Hendel</author>
    <submitter>Gregor Hendel</submitter>
    <author>Matthias Miltenberger</author>
    <author>Jakob Witzig</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6460</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>151</pageFirst>
    <pageLast>157</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploring the Numerics of Branch-and-Cut for Mixed Integer Linear Optimization</title>
    <abstract language="eng">We investigate how the numerical properties of the LP relaxations evolve&#13;
throughout the solution procedure in a solver employing the branch-and-cut&#13;
algorithm. The long-term goal of this work is to determine whether the effect&#13;
on the numerical conditioning of the LP relaxations resulting from the&#13;
branching and cutting operations can be effectively predicted&#13;
and whether such predictions can be used to make better algorithmic&#13;
choices. In a first step towards this goal, we discuss here the numerical&#13;
behavior of an existing solver in order to determine whether our &#13;
intuitive understanding of this behavior is correct.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2017</parentTitle>
    <identifier type="doi">10.1007/978-3-319-89920-6</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-64645</enrichment>
    <enrichment key="AcceptedDate">2017-11-10</enrichment>
    <enrichment key="SourceTitle">Operations Research Proceedings 2017</enrichment>
    <author>Matthias Miltenberger</author>
    <submitter>Matthias Miltenberger</submitter>
    <author>Ted Ralphs</author>
    <author>Daniel Steffy</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6045</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>301</pageFirst>
    <pageLast>307</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>9725</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite</title>
    <abstract language="eng">SCIP is a solver for a wide variety of mathematical optimization problems. It is written in C and extendable due to its plug-in based design. However, dealing with all C specifics when extending SCIP can be detrimental to development and testing of new ideas. This paper attempts to provide a remedy by introducing PySCIPOpt, a Python interface to SCIP that enables users to write new SCIP code entirely in Python. We demonstrate how to intuitively model mixed-integer linear and quadratic optimization problems and moreover provide examples on how new Python plug-ins can be added to SCIP.</abstract>
    <parentTitle language="eng">Mathematical Software – ICMS 2016</parentTitle>
    <identifier type="doi">10.1007/978-3-319-42432-3_37</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-61348</enrichment>
    <author>Stephen J. Maher</author>
    <submitter>Daniel Rehfeldt</submitter>
    <author>Matthias Miltenberger</author>
    <author>João Pedro Pedroso</author>
    <author>Daniel Rehfeldt</author>
    <author>Robert Schwarz</author>
    <author>Felipe Serrano</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="persons" number="rehfeldt">Rehfeldt, Daniel</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
