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  <doc>
    <id>6618</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-12-18</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimizing Large-Scale Linear Energy System Problems with Block Diagonal Structure by Using Parallel Interior-Point Methods</title>
    <abstract language="eng">Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-66183</identifier>
    <identifier type="doi">10.1007/978-3-319-89920-6_85</identifier>
    <enrichment key="SourceTitle">Operations Research Proceedings 2017</enrichment>
    <author>Thomas Breuer</author>
    <submitter>Daniel Rehfeldt</submitter>
    <author>Michael Bussieck</author>
    <author>Karl-Kien Cao</author>
    <author>Felix Cebulla</author>
    <author>Frederik Fiand</author>
    <author>Hans Christian Gils</author>
    <author>Ambros Gleixner</author>
    <author>Dmitry Khabi</author>
    <author>Thorsten Koch</author>
    <author>Daniel Rehfeldt</author>
    <author>Manuel Wetzel</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-75</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy system models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>interior-point methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>high-performance computing</value>
    </subject>
    <collection role="ccs" number="D.">Software</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="rehfeldt">Rehfeldt, Daniel</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>
    <file>https://opus4.kobv.de/opus4-zib/files/6618/zibreport.pdf</file>
  </doc>
</export-example>
