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    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst>47</pageFirst>
    <pageLast>78</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>61</volume>
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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>
    <enrichment key="PeerReviewed">yes</enrichment>
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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>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>1813</id>
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    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst/>
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    <edition/>
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    <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>
    </subject>
    <collection role="msc" number="68W10">Parallel algorithms</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <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>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="persons" number="michael.winkler">Winkler, Michael</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1813/para-scip.pdf</file>
  </doc>
  <doc>
    <id>4994</id>
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    <language>eng</language>
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    <publishedDate>2014-05-13</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
  </doc>
  <doc>
    <id>5080</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
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    <language>eng</language>
    <pageFirst>135</pageFirst>
    <pageLast>146</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>1</volume>
    <type>incollection</type>
    <publisherName>European Mathematical Society</publisherName>
    <publisherPlace/>
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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>
    <collection role="persons" number="groetschel">Grötschel, Martin</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>7802</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2020-03-30</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 7.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for&#13;
mathematical optimization centered around the constraint integer programming frame-&#13;
work SCIP. This paper discusses enhancements and extensions contained in version 7.0&#13;
of the SCIP Optimization Suite. The new version features the parallel presolving library&#13;
PaPILO as a new addition to the suite. PaPILO 1.0 simplifies mixed-integer linear op-&#13;
timization problems and can be used stand-alone or integrated into SCIP via a presolver&#13;
plugin. SCIP 7.0 provides additional support for decomposition algorithms. Besides im-&#13;
provements in the Benders’ decomposition solver of SCIP, user-defined decomposition&#13;
structures can be read, which are used by the automated Benders’ decomposition solver&#13;
and two primal heuristics. Additionally, SCIP 7.0 comes with a tree size estimation&#13;
that is used to predict the completion of the overall solving process and potentially&#13;
trigger restarts. Moreover, substantial performance improvements of the MIP core were&#13;
achieved by new developments in presolving, primal heuristics, branching rules, conflict&#13;
analysis, and symmetry handling. Last, not least, the report presents updates to other&#13;
components and extensions of the SCIP Optimization Suite, in particular, the LP solver&#13;
SoPlex and the mixed-integer semidefinite programming solver SCIP-SDP.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-78023</identifier>
    <author>Gerald Gamrath</author>
    <submitter>Felipe Serrano</submitter>
    <author>Daniel Anderson</author>
    <author>Ksenia Bestuzheva</author>
    <author>Wei-Kun Chen</author>
    <author>Leon Eifler</author>
    <author>Maxime Gasse</author>
    <author>Patrick Gemander</author>
    <author>Ambros Gleixner</author>
    <author>Leona Gottwald</author>
    <author>Katrin Halbig</author>
    <author>Gregor Hendel</author>
    <author>Christopher Hojny</author>
    <author>Thorsten Koch</author>
    <author>Pierre Le Bodic</author>
    <author>Stephen J. Maher</author>
    <author>Frederic Matter</author>
    <author>Matthias Miltenberger</author>
    <author>Erik Mühmer</author>
    <author>Benjamin Müller</author>
    <author>Marc Pfetsch</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Christine Tawfik</author>
    <author>Stefan Vigerske</author>
    <author>Fabian Wegscheider</author>
    <author>Dieter Weninger</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>20-10</number>
    </series>
    <collection role="msc" number="65-XX">NUMERICAL ANALYSIS</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="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="robert.gottwald">Gottwald, Robert</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</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="persons" number="schloesser">Schlösser, Franziska</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
    <collection role="projects" number="plan4res">Plan4res</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7802/scipopt-70.pdf</file>
  </doc>
  <doc>
    <id>6629</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>2017-12-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 5.0</title>
    <abstract language="eng">This article describes new features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite. In its central component, the constraint integer programming solver SCIP, remarkable performance improvements have been achieved for solving mixed-integer linear and nonlinear programs. On MIPs, SCIP 5.0 is about 41 % faster than SCIP 4.0 and over twice as fast on instances that take at least 100 seconds to solve. For MINLP, SCIP 5.0 is about 17 % faster overall and 23 % faster on instances that take at least 100 seconds to solve. This boost is due to algorithmic advances in several parts of the solver such as cutting plane generation and management, a new adaptive coordination of large neighborhood search heuristics, symmetry handling, and strengthened McCormick relaxations for bilinear terms in MINLPs. Besides discussing the theoretical background and the implementational aspects of these developments, the report describes recent additions for the other software packages connected to SCIP, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solver SCIP-SDP, and the parallelization framework UG.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-66297</identifier>
    <author>Ambros Gleixner</author>
    <submitter>Gregor Hendel</submitter>
    <author>Leon Eifler</author>
    <author>Tristan Gally</author>
    <author>Gerald Gamrath</author>
    <author>Patrick Gemander</author>
    <author>Robert Lion Gottwald</author>
    <author>Gregor Hendel</author>
    <author>Christopher Hojny</author>
    <author>Thorsten Koch</author>
    <author>Matthias Miltenberger</author>
    <author>Benjamin Müller</author>
    <author>Marc Pfetsch</author>
    <author>Christian Puchert</author>
    <author>Daniel Rehfeldt</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Jan Merlin Viernickel</author>
    <author>Stefan Vigerske</author>
    <author>Dieter Weninger</author>
    <author>Jonas T. Witt</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-61</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>constraint integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer nonlinear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimization solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-cut</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-price</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>column generation framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallelization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer semidefinite programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
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    <collection role="msc" number="65-XX">NUMERICAL ANALYSIS</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
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    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="robert.gottwald">Gottwald, Robert</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="persons" number="rehfeldt">Rehfeldt, Daniel</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
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  <doc>
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    <publishedYear>2012</publishedYear>
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    <title language="eng">Comparing MIQCP Solvers to a Specialised Algorithm for Mine Production Scheduling</title>
    <abstract language="eng">In this paper we investigate the performance of several out-of-the box solvers for mixed-integer quadratically constrained programmes (MIQCPs) on an open pit mine production scheduling problem with mixing constraints. We compare the solvers BARON, Couenne, SBB, and SCIP to a problem-specific algorithm on two different MIQCP formulations. The computational results presented show that general-purpose solvers with no particular knowledge of problem structure are able to nearly match the performance of a hand-crafted algorithm.</abstract>
    <parentTitle language="eng">Modeling, Simulation and Optimization of Complex Processes. Proceedings of the Fourth International Conference on High Performance Scientific Computing, March 2-6, 2009, Hanoi, Vietnam</parentTitle>
    <identifier type="doi">10.1007/978-3-642-25707-0_3</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-11507</enrichment>
    <author>Andreas Bley</author>
    <submitter> Engel</submitter>
    <author>Ambros Gleixner</author>
    <author>Thorsten Koch</author>
    <author>Stefan Vigerske</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MATHEON-B20">MATHEON-B20</collection>
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  </doc>
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    <completedDate>2009-10-15</completedDate>
    <publishedDate>2009-10-15</publishedDate>
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    <title language="eng">Comparing MIQCP solvers to a specialised algorithm for mine production scheduling</title>
    <abstract language="eng">In this paper we investigate the performance of several out-of-the box solvers for mixed-integer quadratically constrained programmes (MIQCPs) on an open pit mine production scheduling problem with mixing constraints. We compare the solvers BARON, Couenne, SBB, and SCIP to a problem-specific algorithm on two different MIQCP formulations. The computational results presented show that general-purpose solvers with no particular knowledge of problem structure are able to nearly match the performance of a hand-crafted algorithm.</abstract>
    <identifier type="serial">09-32</identifier>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="opus3-id">1206</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-11507</identifier>
    <identifier type="doi">10.1007/978-3-642-25707-0</identifier>
    <enrichment key="SourceTitle">Appeared in: Modeling, Simulation and Optimization of Complex Processes, Proc. of the 4th Int. Conf. on High Performance Scientific Computing, 2009, Hanoi, Vietnam. H. G. Bock et al. (eds.) 2012, pp. 25-39</enrichment>
    <author>Andreas Bley</author>
    <submitter>unknown unknown</submitter>
    <author>Ambros Gleixner</author>
    <author>Thorsten Koch</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>09-32</number>
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    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="msc" number="90B30">Production models</collection>
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    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C26">Nonconvex programming, global optimization</collection>
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    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
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  <doc>
    <id>4718</id>
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    <publishedYear>2014</publishedYear>
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    <title language="eng">Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions</title>
    <abstract language="eng">In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints.&#13;
&#13;
We describe a two-stage approach to solve the resulting complex and numerically difficult feasibility problem. The first phase consists of four distinct algorithms applying linear, and methods for complementarity constraints to compute possible settings for the discrete decisions. The second phase employs a precise continuous programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances that are significantly larger than networks that have appeared in the mathematical programming literature before.</abstract>
    <parentTitle language="eng">Optimization Methods and Software</parentTitle>
    <identifier type="doi">10.1080/10556788.2014.888426</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-16531</enrichment>
    <submitter>Robert Schwarz</submitter>
    <author>Marc Pfetsch</author>
    <author>Armin Fügenschuh</author>
    <author>Björn Geißler</author>
    <author>Nina Geißler</author>
    <author>Ralf Gollmer</author>
    <author>Benjamin Hiller</author>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Alexander Martin</author>
    <author>Antonio Morsi</author>
    <author>Jessica Rövekamp</author>
    <author>Lars Schewe</author>
    <author>Martin Schmidt</author>
    <author>Rüdiger Schultz</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Claudia Stangl</author>
    <author>Marc Steinbach</author>
    <author>Stefan Vigerske</author>
    <author>Bernhard Willert</author>
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    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
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    <publishedDate>2016-02-26</publishedDate>
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    <title language="eng">The SCIP Optimization Suite 3.2</title>
    <abstract language="eng">The SCIP Optimization Suite is a software toolbox for generating and solving various classes of mathematical optimization problems. Its major components are the modeling language ZIMPL, the linear programming solver SoPlex, the constraint integer programming framework and mixed-integer linear and nonlinear programming solver SCIP, the UG framework for parallelization of branch-and-bound-based solvers, and the generic branch-cut-and-price solver GCG. It has been used in many applications from both academia and industry and is one of the leading non-commercial solvers.&#13;
This paper highlights the new features of version 3.2 of the SCIP Optimization Suite. Version 3.2 was released in July 2015. This release comes with new presolving steps, primal heuristics, and branching rules within SCIP. In addition, version 3.2 includes a reoptimization feature and improved handling of quadratic constraints and special ordered sets. SoPlex can now solve LPs exactly over the rational number and performance improvements have been achieved by exploiting sparsity in more situations. UG has been tested successfully on 80,000 cores. A major new feature of UG is the functionality to parallelize a customized SCIP solver. GCG has been enhanced with a new separator, new primal heuristics, and improved column management. Finally, new and improved extensions of SCIP are presented, namely solvers for multi-criteria optimization, Steiner tree problems, and mixed-integer semidefinite programs.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-57675</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <author>Tobias Fischer</author>
    <author>Tristan Gally</author>
    <author>Ambros Gleixner</author>
    <author>Gregor Hendel</author>
    <author>Thorsten Koch</author>
    <author>Stephen J. Maher</author>
    <author>Matthias Miltenberger</author>
    <author>Benjamin Müller</author>
    <author>Marc Pfetsch</author>
    <author>Christian Puchert</author>
    <author>Daniel Rehfeldt</author>
    <author>Sebastian Schenker</author>
    <author>Robert Schwarz</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Stefan Vigerske</author>
    <author>Dieter Weninger</author>
    <author>Michael Winkler</author>
    <author>Jonas T. Witt</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-60</number>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer linear and nonlinear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MIP solver</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MINLP solver</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LP solver</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simplex method</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallel branch-and-bound</value>
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    <subject>
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      <type>uncontrolled</type>
      <value>branch-cut-and-price framework</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>generic column generation</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Steiner tree solver</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-criteria optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer semidefinite programming</value>
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    <file>https://opus4.kobv.de/opus4-zib/files/5767/scipopt-32.pdf</file>
  </doc>
  <doc>
    <id>1653</id>
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    <completedDate>2012-11-19</completedDate>
    <publishedDate>2012-11-19</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions</title>
    <abstract language="eng">In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints.&#13;
We describe a two-stage approach to solve the resulting complex and numerically difficult mixed-integer non-convex nonlinear feasibility problem. The first phase consists of four distinct algorithms facilitating mixed-integer linear, mixed-integer nonlinear, reduced nonlinear, and complementarity constrained methods to compute possible settings for the discrete decisions. The second phase employs a precise continuous nonlinear programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances whose size is significantly larger than networks that have appeared  in the literature previously.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-16531</identifier>
    <identifier type="doi">10.1080/10556788.2014.888426</identifier>
    <enrichment key="SourceTitle">Appeared in: Optimization Methods and Software 30 (2015) pp. 15-53</enrichment>
    <author>Marc Pfetsch</author>
    <submitter>Jonas Schweiger</submitter>
    <author>Armin Fügenschuh</author>
    <author>Björn Geißler</author>
    <author>Nina Geißler</author>
    <author>Ralf Gollmer</author>
    <author>Benjamin Hiller</author>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Alexander Martin</author>
    <author>Antonio Morsi</author>
    <author>Jessica Rövekamp</author>
    <author>Lars Schewe</author>
    <author>Martin Schmidt</author>
    <author>Rüdiger Schultz</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Claudia Stangl</author>
    <author>Marc Steinbach</author>
    <author>Stefan Vigerske</author>
    <author>Bernhard Willert</author>
    <series>
      <title>ZIB-Report</title>
      <number>12-41</number>
    </series>
    <collection role="ccs" number="D.">Software</collection>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="msc" number="90-08">Computational methods</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C30">Nonlinear programming</collection>
    <collection role="msc" number="90C90">Applications of mathematical programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="ForNe">ForNe</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
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    <file>https://opus4.kobv.de/opus4-zib/files/1653/ZR-12-41.pdf</file>
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    <id>8530</id>
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    <publishedDate>2021-12-17</publishedDate>
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    <title language="eng">The SCIP Optimization Suite 8.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 8.0 of the SCIP Optimization Suite. Major updates in SCIP include improvements in symmetry handling and decomposition algorithms, new cutting planes, a new plugin type for cut selection, and a complete rework of the way nonlinear constraints are handled. Additionally, SCIP 8.0 now supports interfaces for Julia as well as Matlab. Further, UG now includes a unified framework to parallelize all solvers, a utility to analyze computational experiments has been added to GCG, dual solutions can be postsolved by PaPILO, new heuristics and presolving methods were added to SCIP-SDP, and additional problem classes and major performance improvements are available in SCIP-Jack.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-85309</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Ksenia Bestuzheva</author>
    <submitter>Ksenia Bestuzheva</submitter>
    <author>Mathieu Besançon</author>
    <author>Wei-Kun Chen</author>
    <author>Antonia Chmiela</author>
    <author>Tim Donkiewicz</author>
    <author>Jasper van Doornmalen</author>
    <author>Leon Eifler</author>
    <author>Oliver Gaul</author>
    <author>Gerald Gamrath</author>
    <author>Ambros Gleixner</author>
    <author>Leona Gottwald</author>
    <author>Christoph Graczyk</author>
    <author>Katrin Halbig</author>
    <author>Alexander Hoen</author>
    <author>Christopher Hojny</author>
    <author>Rolf van der Hulst</author>
    <author>Thorsten Koch</author>
    <author>Marco Lübbecke</author>
    <author>Stephen J. Maher</author>
    <author>Frederic Matter</author>
    <author>Erik Mühmer</author>
    <author>Benjamin Müller</author>
    <author>Marc E. Pfetsch</author>
    <author>Daniel Rehfeldt</author>
    <author>Steffan Schlein</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Boro Sofranac</author>
    <author>Mark Turner</author>
    <author>Stefan Vigerske</author>
    <author>Fabian Wegscheider</author>
    <author>Philipp Wellner</author>
    <author>Dieter Weninger</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>21-41</number>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Constraint integer programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linear programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer linear programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer nonlinear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimization solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-cut</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-price</value>
    </subject>
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    <title language="eng">The SCIP Optimization Suite 9.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.</abstract>
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      <title>ZIB-Report</title>
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      <value>Mixed-integer semidefinite programming</value>
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    <title language="eng">Enabling research through the SCIP optimization suite 8.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. The focus of this article is on the role of the SCIP Optimization Suite in supporting research. SCIP’s main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP’s application as a research tool and as a platform for further developments. Furthermore, this article gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP.</abstract>
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