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    <title language="jpn">制約整数計画ソルバ SCIP の並列化</title>
    <abstract language="jpn">制約整数計画（CIP: Constraint Integer Programs）は，制約プログラミング（CP: Constraint Programming），混合整数計画（MIP: Mixed Integer Programming），充足可能性問題（SAT: Satisfability Problem）の研究分野におけるモデリング技術と解法を統合している．その結果，制約整数計画は，広いクラスの最適化問題を扱うことができる．SCIP（Solving Constraint Integer Programs）は，CIP を解くソルバとして実装され，Zuse Institute Berlin（ZIB）の研究者を中心として継続的に拡張が続けられている．本論文では，著者らによって開発された SCIP に対する2 種類の並列化拡張を紹介する．一つは，複数計算ノード間で大規模に並列動作する ParaSCIPである．もう一つは，複数コアと共有メモリを持つ 1 台の計算機上で（スレッド）並列で動作する FiberSCIP である．ParaSCIP は，HLRN II スーパーコンピュータ上で，一つのインスタンスを解くために最大 7,168 コアを利用した動作実績がある．また，統計数理研究所の Fujitsu PRIMERGY RX200S5 上でも，最大 512 コアを利用した動作実績がある．統計数理研究所のFujitsu PRIMERGY RX200S5 上では，これまでに最適解が得られていなかった MIPLIB2010のインスタンスである dg012142 に最適解を与えた．</abstract>
    <abstract language="eng">The paradigm of constraint integer programming (CIP) combines modeling and solving techniques from the fields of constraint programming (CP), mixed-integer programming (MIP) and　satisfability problem (SAT). This paradigm allows us to address a wide　range of optimization problems. SCIP is an implementation of the idea of CIP and is now being continuously extended by a group of researchers centered at Zuse Institute Berlin (ZIB). This paper introduces two parallel extensions of SCIP. One is ParaSCIP, which is intended to run on a large scale distributed memory computing environment, and the other is FiberSCIP, intended to run on a shared memory computing environment. ParaSCIP has been run successfully on the HLRN II supercomputer utilizing up to 7,168 cores to solve a single difficult MIP. It has also been tested on an ISM supercomputer (Fujitsu PRIMERGY RX200S5 using up to 512 cores). The previously unsolved instance dg012142 from MIPLIB2010 was solved by using the ISM supercomputer.</abstract>
    <parentTitle language="jpn">統計数理</parentTitle>
    <identifier type="url">https://www.ism.ac.jp/editsec/toukei/pdf/61-1-047.pdf</identifier>
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    <author>Yuji Shinano</author>
    <submitter>Yuji Shinano</submitter>
    <author>Tobias Achterberg</author>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Thorsten Koch</author>
    <author>Stefan Vigerske</author>
    <author>Michael Winkler</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
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    <language>eng</language>
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    <completedDate>2013-02-14</completedDate>
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    <title language="eng">Analyzing the computational impact of MIQCP solver components</title>
    <abstract language="eng">We provide a computational study of the performance of a state-of-the-art solver for nonconvex mixed-integer quadratically constrained programs (MIQCPs). Since successful general-purpose solvers for large problem classes necessarily comprise a variety of algorithmic techniques, we focus especially on the impact of the individual solver components. The solver SCIP used for the experiments implements a branch-and-cut algorithm based on a linear relaxation to solve MIQCPs to global optimality. Our analysis is based on a set of 86 publicly available test instances.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-17754</identifier>
    <identifier type="doi">10.3934/naco.2012.2.739</identifier>
    <enrichment key="SourceTitle">Appeared in: Numerical Algebra, Control and Optimization vol. 2, no. 4 (2012) pp. 739-748</enrichment>
    <author>Timo Berthold</author>
    <submitter>Timo Berthold</submitter>
    <author>Ambros Gleixner</author>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-08</number>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer quadratically constrained programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-cut</value>
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      <language>eng</language>
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      <value>nonconvex</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global optimization</value>
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    <collection role="msc" number="90C26">Nonconvex programming, global optimization</collection>
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    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
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    <completedDate>2011-03-16</completedDate>
    <publishedDate>2011-03-16</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On the computational impact of MIQCP solver components</title>
    <abstract language="eng">We provide a computational study of the performance of a state-of-the-art solver for nonconvex mixed-integer quadratically constrained programs (MIQCPs). Since successful general-purpose solvers for large problem classes necessarily comprise a variety of algorithmic techniques, we focus especially on the impact of the individual solver components. The solver SCIP used for the experiments implements a branch-and-cut algorithm based on linear outer approximation to solve MIQCPs to global optimality. Our analysis is based on a set of 86 publicly available test instances.</abstract>
    <identifier type="serial">11-01</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-11998</identifier>
    <author>Timo Berthold</author>
    <submitter>-empty- (Opus4 user: )</submitter>
    <author>Ambros Gleixner</author>
    <submitter>Timo Berthold</submitter>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>11-01</number>
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    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>MIQCP</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>MIP</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>mixed-integer quadratically constrained programming</value>
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    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>computational</value>
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    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>nonconvex</value>
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    <collection role="ccs" number="G.4">MATHEMATICAL SOFTWARE</collection>
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  <doc>
    <id>4818</id>
    <completedYear>2012</completedYear>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted>2012</thesisYearAccepted>
    <language>eng</language>
    <pageFirst>739</pageFirst>
    <pageLast>748</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>2</volume>
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    <completedDate>--</completedDate>
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    <title language="eng">Analyzing the computational impact of MIQCP solver components</title>
    <abstract language="eng">We provide a computational study of the performance of a state-of-the-art solver for nonconvex mixed-integer quadratically constrained programs (MIQCPs). Since successful general-purpose solvers for large problem classes necessarily comprise a variety of algorithmic techniques, we focus especially on the impact of the individual solver components. The solver SCIP used for the experiments implements a branch-and-cut algorithm based on a linear relaxation to solve MIQCPs to global optimality. Our analysis is based on a set of 86 publicly available test instances.</abstract>
    <parentTitle language="eng">Numerical Algebra, Control and Optimization</parentTitle>
    <identifier type="doi">10.3934/naco.2012.2.739</identifier>
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    <submitter>Ambros Gleixner</submitter>
    <author>Timo Berthold</author>
    <author>Ambros Gleixner</author>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
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    <id>4761</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
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    <language>eng</language>
    <pageFirst>427</pageFirst>
    <pageLast>444</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>154</volume>
    <type>incollection</type>
    <publisherName>Springer</publisherName>
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    <completedDate>--</completedDate>
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    <title language="eng">Extending a CIP framework to solve MIQCPs</title>
    <abstract language="eng">This paper discusses how to build a solver for mixed integer quadratically constrained programs (MIQCPs) by extending a framework for constraint integer programming (CIP). The advantage of this approach is that we can utilize the full power of advanced MIP and CP technologies. In particular, this addresses the linear relaxation and the discrete components of the problem. For relaxation, we use an outer approximation generated by linearization of convex constraints and linear underestimation of nonconvex constraints. Further, we give an overview of the reformulation, separation, and propagation techniques that are used to handle the quadratic constraints efficiently. We implemented these methods in the branch-cut-and-price framework SCIP. Computational experiments indicates the potential of the approach.</abstract>
    <parentTitle language="eng">Mixed Integer Nonlinear Programming</parentTitle>
    <identifier type="isbn">978-1-4614-1927-3</identifier>
    <enrichment key="Series">The IMA Volumes in Mathematics and its Applications</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-11371</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Timo Berthold</author>
    <editor>Jon Lee</editor>
    <submitter> Engel</submitter>
    <author>Stefan Heinz</author>
    <editor>Sven Leyffer</editor>
    <author>Stefan Vigerske</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
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    <publishedYear>2011</publishedYear>
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    <completedDate>--</completedDate>
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    <title language="eng">Large Neighborhood Search beyond MIP</title>
    <abstract language="eng">Large neighborhood search (LNS) heuristics are an important component of modern branch-and-cut algorithms for solving mixed-integer linear programs (MIPs). Most of these LNS heuristics use the LP relaxation as the basis for their search, which is a reasonable choice in case of MIPs. However, for more general problem classes, the LP relaxation alone may not contain enough information about the original problem to find feasible solutions with these heuristics, e.g., if the problem is nonlinear or not all constraints are present in the current relaxation. In this paper, we discuss a generic way to extend LNS heuristics that have been developed for MIP to constraint integer programming (CIP), which is a generalization of MIP in the direction of constraint programming (CP). We present computational results of LNS heuristics for three problem classes: mixed-integer quadratically constrained programs, nonlinear pseudo-Boolean optimization instances, and resource-constrained project scheduling problems. Therefore, we have implemented extended versions of the following LNS heuristics in the constraint integer programming framework SCIP: Local Branching, RINS, RENS, Crossover, and DINS. Our results indicate that a generic generalization of LNS heuristics to CIP considerably improves the success rate of these heuristics.</abstract>
    <parentTitle language="eng">Proceedings of the 9th Metaheuristics International Conference (MIC 2011)</parentTitle>
    <identifier type="isbn">978-88-900984-3-7</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-12989</enrichment>
    <author>Timo Berthold</author>
    <submitter> Engel</submitter>
    <author>Stefan Heinz</author>
    <author>Marc Pfetsch</author>
    <author>Stefan Vigerske</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
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    <language>jpn</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2013-04-22</completedDate>
    <publishedDate>2013-04-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="jpn">制約整数計画ソルバ SCIP の並列化</title>
    <title language="eng">Parallelizing the Constraint Integer Programming Solver SCIP</title>
    <abstract language="jpn">制約整数計画(CIP: Constraint Integer Programming)は，制約プログラミング(CP: Constraint Programming)，混合整数計画(MIP: Mixed Integer Programming), 充足可能性問題(SAT: Satisfiability Problems)の研究分野におけるモデリング技術と解法を統合している．その結果，制約整数計画は，広いクラスの最適化問題を扱うことができる．SCIP (Solving Constraint Integer Programs)は，CIPを解くソルバとして実装され,Zuse Institute Berlin (ZIB)の研究者を中心として継続的に拡張が続けられている．本論文では，著者らによって開発されたSCIP に対する2種類の並列化拡張を紹介する． 一つは，複数計算ノード間で大規模に並列動作するParaSCIP である． もう一つは，複数コアと共有メモリを持つ１台の計算機上で(スレッド)並列で動作するFiberSCIP である． ParaSCIP は，HLRN IIスーパーコンピュータ上で， 一つのインスタンスを解くために最大7,168 コアを利用した動作実績がある．また，統計数理研究所のFujitsu PRIMERGY RX200S5上でも，最大512コアを利用した動作実績がある．統計数理研究所のFujitsu PRIMERGY RX200S5上 では，これまでに最適解が得られていなかったMIPLIB2010のインスタンスであるdg012142に最適解を与えた．</abstract>
    <abstract language="eng">The paradigm of Constraint Integer Programming (CIP) combines modeling and solving techniques from the fields of Constraint Programming (CP), Mixed Integer Programming (MIP) and Satisfiability Problems (SAT). The paradigm allows us to address a wide range of optimization problems. SCIP is an implementation of the idea of CIP and is now continuously extended by a group of researchers centered at Zuse Institute Berlin (ZIB). This paper introduces two parallel extensions of SCIP. One is ParaSCIP, which is intended to run on a large scale distributed memory computing environment, and the other is FiberSCIP, intended to run on shared memory computing environments. ParaSCIP has successfully been run on the HLRN II supercomputer utilizing up to 7,168 cores to solve a single difficult MIP. It has also been tested on an ISM supercomputer (Fujitsu PRIMERGY RX200S5 using up to 512 cores). The previously unsolved instance dg012142 from MIPLIB2010 was solved by using the ISM supercomputer.</abstract>
    <additionalTitle language="eng">Parallelizing the Constraint Integer Programming Solver SCIP</additionalTitle>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-18130</identifier>
    <author>Yuji Shinano</author>
    <submitter>Yuji Shinano</submitter>
    <author>Tobias Achterberg</author>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Thorsten Koch</author>
    <author>Stefan Vigerske</author>
    <author>Michael Winkler</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-22</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed Integer Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Constraint Integer Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parallel Computing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Distributed Memory</value>
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    <collection role="msc" number="68W10">Parallel algorithms</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
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    <collection role="persons" number="achterberg">Achterberg, Tobias</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
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    <title language="eng">Large Neighborhood Search beyond MIP</title>
    <abstract language="eng">Large neighborhood search (LNS) heuristics are an important component of modern branch-and-cut algorithms for solving mixed-integer linear programs (MIPs). Most of these LNS heuristics use the LP relaxation as the basis for their search, which is a reasonable choice in case of MIPs. However, for more general problem classes, the LP relaxation alone may not contain enough information about the original problem to find feasible solutions with these heuristics, e.g., if the problem is nonlinear or not all constraints are present in the current relaxation.&#13;
&#13;
In this paper, we discuss a generic way to extend LNS heuristics that have been developed for MIP to constraint integer programming (CIP), which is a generalization of MIP in the direction of constraint programming (CP). We present computational results of LNS heuristics for three problem classes: mixed-integer quadratically constrained programs, nonlinear pseudo-Boolean optimization instances, and resource-constrained project scheduling problems. Therefore, we have implemented extended versions of the following LNS heuristics in the constraint integer programming framework SCIP: Local Branching, RINS, RENS, Crossover, and DINS. Our results indicate that a generic generalization of LNS heuristics to CIP considerably improves the success rate of these heuristics.</abstract>
    <identifier type="serial">11-21</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-12989</identifier>
    <enrichment key="SourceTitle">Appeared in: Proceedings of the 9th Metaheuristics International Conference (MIC 2011).  2011. Luca di Gaspar et al. eds. ISBN 978-88-900984-3-7, pp. 51-60</enrichment>
    <author>Timo Berthold</author>
    <submitter>Timo Berthold</submitter>
    <author>Stefan Heinz</author>
    <author>Marc Pfetsch</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>11-21</number>
    </series>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Large Neighborhood Search</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Primal Heuristic</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>MIP</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>MIQCP</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Pseudo-Boolean</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1298/lns4cip.pdf</file>
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  <doc>
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    <completedDate>2009-07-09</completedDate>
    <publishedDate>2009-07-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Extending a CIP framework to solve MIQCPs</title>
    <abstract language="eng">This paper discusses how to build a solver for mixed integer quadratically constrained programs (MIQCPs) by extending a framework for constraint integer programming (CIP). The advantage of this approach is that we can utilize the full power of advanced MIP and CP technologies. In particular, this addresses the linear relaxation and the discrete components of the problem. For relaxation, we use an outer approximation generated by linearization of convex constraints and linear underestimation of nonconvex constraints. Further, we give an overview of the reformulation, separation, and propagation techniques that are used to handle the quadratic constraints efficiently. We implemented these methods in the branch-cut-and-price framework SCIP. Computational experiments indicates the potential of the approach.</abstract>
    <identifier type="serial">09-23</identifier>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="opus3-id">1186</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-11371</identifier>
    <enrichment key="SourceTitle">App. in: Mixed Integer Nonlinear Programming. Jon Lee, Sven Leyffer (eds.) The IMA Volumes in Mathematics and its Applications, 154. Springer 2011, pp. 427-444</enrichment>
    <author>Timo Berthold</author>
    <submitter>unknown unknown</submitter>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>09-23</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer quadratically constrained programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>constraint integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>convex relaxation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nonconvex</value>
    </subject>
    <collection role="ddc" number="510">Mathematik</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C20">Quadratic programming</collection>
    <collection role="msc" number="90C26">Nonconvex programming, global optimization</collection>
    <collection role="msc" number="90C27">Combinatorial optimization</collection>
    <collection role="msc" number="90C57">Polyhedral combinatorics, branch-and-bound, branch-and-cut</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
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    <publishedDate>2014-05-13</publishedDate>
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    <title language="eng">A Jack of all Trades? Solving stochastic mixed-integer nonlinear constraint programs</title>
    <abstract language="eng">Natural gas is one of the most important energy sources in Germany and Europe.  In recent years, political regulations have led to a strict separation of gas trading and gas transport, thereby assigning a central role in energy politics to the transportation and distribution of gas. These newly imposed political requirements influenced the technical processes of gas transport in such a way that the complex task of planning and operating gas networks has become even more intricate.&#13;
&#13;
Mathematically, the combination of discrete decisions on the configuration of a gas transport network, the nonlinear equations describing the physics of gas, and the uncertainty in demand and supply yield large-scale and highly complex stochastic mixed-integer nonlinear optimization problems.&#13;
&#13;
The Matheon project "Optimization of Gas Transport" takes the key role of making available the necessary core technology to solve the mathematical optimization problems which model the topology planning and the operation of gas networks. An important aspect of the academic impact is the free availability of our framework. As a result of several years of research and development, it is now possible to download a complete state-of-the-art framework for mixed-integer linear and nonlinear programming in source code at http://scip.zib.de</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-49947</identifier>
    <identifier type="doi">10.4171/137</identifier>
    <enrichment key="SourceTitle">Appeared in: Matheon - Mathematics for Key Technologies. EMS 2014, pp. 135-146</enrichment>
    <author>Thomas Arnold</author>
    <submitter>Timo Berthold</submitter>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <author>René Henrion</author>
    <author>Martin Grötschel</author>
    <author>Thorsten Koch</author>
    <author>Caren Tischendorf</author>
    <author>Werner Römisch</author>
    <series>
      <title>ZIB-Report</title>
      <number>14-14</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gas transport optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer nonlinear programming</value>
    </subject>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/4994/B-koch-thorsten.pdf</file>
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    <volume>1</volume>
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    <completedDate>--</completedDate>
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    <title language="eng">A Jack of all Trades? Solving stochastic mixed-integer nonlinear constraint programs</title>
    <abstract language="eng">Natural gas is one of the most important energy sources in Germany and Europe. In recent years, political regulations have led to a strict separation of gas trading and gas transport, thereby assigning a central role in energy politics to the transportation and distribution of gas. These newly imposed political requirements influenced the technical processes of gas transport in such a way that the complex task of planning and operating gas networks has become even more intricate. Mathematically, the combination of discrete decisions on the configuration of a gas transport network, the nonlinear equations describing the physics of gas, and the uncertainty in demand and supply yield large-scale and highly complex stochastic mixed-integer nonlinear optimization problems. The Matheon project "Optimization of Gas Transport" takes the key role of making available the necessary core technology to solve the mathematical optimization problems which model the topology planning and the operation of gas networks. An important aspect of the academic impact is the free availability of our framework. As a result of several years of research and development, it is now possible to download a complete state-of-the-art framework for mixed-integer linear and nonlinear programming in source code at http://scip.zib.de</abstract>
    <parentTitle language="eng">MATHEON - Mathematics for Key Technologies</parentTitle>
    <identifier type="doi">10.4171/137</identifier>
    <enrichment key="Series">EMS Series in Industrial and Applied Mathematics</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-49947</enrichment>
    <editor>Peter Deuflhard</editor>
    <author>Thomas Arnold</author>
    <submitter>Bettina Kasse</submitter>
    <editor>Martin Grötschel</editor>
    <author>Timo Berthold</author>
    <editor>Dietmar Hömberg</editor>
    <author>Stefan Heinz</author>
    <editor>Ulrich Horst</editor>
    <author>Stefan Vigerske</author>
    <editor>Jürg Kramer</editor>
    <author>René Henrion</author>
    <author>Martin Grötschel</author>
    <editor>Volker Mehrmann</editor>
    <author>Thorsten Koch</author>
    <editor>Konrad Polthier</editor>
    <author>Caren Tischendorf</author>
    <editor>Frank Schmidt</editor>
    <author>Werner Römisch</author>
    <editor>Christof Schütte</editor>
    <editor>Martin Skutella</editor>
    <editor>Jürgen Sprekels</editor>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
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