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<export-example>
  <doc>
    <id>9057</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst>47</pageFirst>
    <pageLast>78</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>61</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="SubmissionStatus">in press</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <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>1775</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-02-14</completedDate>
    <publishedDate>2013-02-14</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer quadratically constrained programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-cut</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nonconvex</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global optimization</value>
    </subject>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C26">Nonconvex programming, global optimization</collection>
    <collection role="msc" number="90C30">Nonlinear 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="projects" number="MATHEON-B20">MATHEON-B20</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1775/ZR-13-08.pdf</file>
  </doc>
  <doc>
    <id>4259</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-09-27</completedDate>
    <publishedDate>2013-09-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">FiberSCIP - A shared memory parallelization of SCIP</title>
    <abstract language="eng">Recently, parallel computing environments have become significantly popular. In order to obtain the benefit of using parallel computing environments, we have to deploy our programs for these effectively. This paper focuses on a parallelization of SCIP (Solving Constraint Integer Programs), which is a MIP solver and constraint integer programming framework available in source code. There is a parallel extension of SCIP named ParaSCIP, which parallelizes SCIP on massively parallel distributed memory computing environments. This paper describes FiberSCIP, which is yet another parallel extension of SCIP to utilize multi-threaded parallel computation on shared memory computing environments, and has the following contributions: First, the basic concept of having two parallel extensions and the relationship between them and the parallelization framework provided by UG (Ubiquity Generator) is presented, including an implementation of deterministic parallelization. Second, the difficulties to achieve a good performance that utilizes all resources on an actual computing environment and the difficulties of performance evaluation of the parallel solvers are discussed. Third, a way to evaluate the performance of new algorithms and parameter settings of the parallel extensions is presented. Finally, current performance of FiberSCIP for solving mixed-integer linear programs (MIPs) and mixed-integer non-linear programs (MINLPs) in parallel is demonstrated.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-42595</identifier>
    <identifier type="doi">10.1287/ijoc.2017.0762</identifier>
    <enrichment key="SourceTitle">INFORMS Journal on Computing</enrichment>
    <author>Yuji Shinano</author>
    <submitter>Yuji Shinano</submitter>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <author>Michael Winkler</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-55</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallel</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-bound</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deterministic parallelism</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>constraint integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer 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>SCIP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MIP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MINLP</value>
    </subject>
    <collection role="ccs" number="D.">Software</collection>
    <collection role="msc" number="68-XX">COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)</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="heinz">Heinz, Stefan</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="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>
    <file>https://opus4.kobv.de/opus4-zib/files/4259/fiberscip-zib.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/4259/fiberscip-zib-rev.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/4259/fiberscip-zib-rev3.pdf</file>
  </doc>
  <doc>
    <id>1764</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-01-14</completedDate>
    <publishedDate>2013-01-14</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Linear Underestimators for bivariate functions with a fixed convexity behavior</title>
    <abstract language="eng">This is a technical report for the SCIP constraint handler cons_bivariate. We describe a cut-generation algorithm for a class of bivariate twice continuously differentiable functions with&#13;
fixed convexity behavior over a box.  &#13;
Computational results comparing our cut-generation algorithms with&#13;
state-of-the-art global  &#13;
optimization software on a series of randomly generated test instances are reported and discussed.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-17641</identifier>
    <author>Martin Ballerstein</author>
    <submitter>Stefan Vigerske</submitter>
    <author>Dennis Michaels</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-02</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bivariate functions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>convex envelope</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear underestimator</value>
    </subject>
    <collection role="ccs" number="">Global optimization (NEW)</collection>
    <collection role="msc" number="90C26">Nonconvex programming, global 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="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</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>
    <file>https://opus4.kobv.de/opus4-zib/files/1764/ZR-13-02.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/1764/ZR-13-02_testset.tgz</file>
    <file>https://opus4.kobv.de/opus4-zib/files/1764/ZR-13-02revised.pdf</file>
  </doc>
  <doc>
    <id>1813</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <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>
    </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>
</export-example>
