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    <title language="eng">Generic Construction and Efficient Evaluation of Network DAEs and Their  Derivatives in the Context of Gas Networks</title>
    <abstract language="eng">We present a concept that provides an efficient description of differential-algebraic equations (DAEs) describing flow networks which provides the DAE function f and their Jacobians in an automatized way such that the sparsity pattern of the Jacobians is determined before their evaluation and previously determined values of f can be exploited. The user only has to provide the network topology and local function descriptions for each network element. The approach uses automatic differentiation (AD) and is adapted to switching element functions via the abs-normal-form (ANF).</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2017</parentTitle>
    <identifier type="doi">10.1007/978-3-319-89920-6_83</identifier>
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    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-64582</enrichment>
    <enrichment key="AcceptedDate">2017-12-08</enrichment>
    <author>Tom Streubel</author>
    <submitter>Tom Streubel</submitter>
    <author>Christian Strohm</author>
    <author>Philipp Trunschke</author>
    <author>Caren Tischendorf</author>
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      <value>algorithmic differentiation</value>
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    <collection role="institutes" number="enernet">Energy Network Optimization</collection>
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    <title language="eng">Generic Construction and Efficient Evaluation of Network DAEs and Their  Derivatives in the Context of Gas Networks</title>
    <abstract language="eng">We present a concept that provides an efficient description of differential-algebraic equations (DAEs) describing flow networks which provides the DAE function f and their Jacobians in an automatized way such that the sparsity pattern of the Jacobians is determined before their evaluation and previously determined values of f can be exploited. The user only has to provide the network topology and local function descriptions for each network element. The approach uses automatic differentiation (AD) and is adapted to switching element functions via the abs-normal-form (ANF).</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-64582</identifier>
    <identifier type="doi">10.1007/978-3-319-89920-6_83</identifier>
    <enrichment key="SourceTitle">published at Operations Research Proceedings 2017</enrichment>
    <author>Tom Streubel</author>
    <submitter>Tom Streubel</submitter>
    <author>Christian Strohm</author>
    <author>Philipp Trunschke</author>
    <author>Caren Tischendorf</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-41</number>
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    <subject>
      <language>eng</language>
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      <value>compressed sparse row format</value>
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      <language>eng</language>
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      <value>algorithmic differentiation</value>
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    <subject>
      <language>eng</language>
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      <value>abs-normal form</value>
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      <value>piecewise linear tangent approximation</value>
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      <value>piecewise smooth</value>
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    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
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    <collection role="institutes" number="enernet">Energy Network Optimization</collection>
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