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  <doc>
    <id>6538</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-10-25</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Structure-driven fix-and-propagate heuristics for mixed integer programming</title>
    <abstract language="eng">Primal heuristics play an important role in the solving of mixed integer programs (MIPs). They often provide good feasible solutions early in the solving process and help to solve instances to optimality faster. In this paper, we present a scheme for primal start heuristics that can be executed without previous knowledge of an LP solution or a previously found integer feasible solution. It uses global structures available within MIP solvers to iteratively fix integer variables and propagate these fixings. Thereby, fixings are determined based on the predicted impact they have on the subsequent domain propagation. If sufficiently many variables can be fixed that way, the resulting problem is solved as an LP and the solution is rounded. If the rounded solution did not provide a feasible solution already, a sub-MIP is solved for the neighborhood defined by the variable fixings performed in the first phase. The global structures help to define a neighborhood that is with high probability significantly easier to process while (hopefully) still containing good feasible solutions. We present three primal heuristics that use this scheme based on different global structures. Our computational experiments on standard MIP test sets show that the proposed heuristics find solutions for about three out of five instances and therewith help to improve several performance measures for MIP solvers, including the primal integral and the average solving time.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-65387</identifier>
    <identifier type="doi">10.1007/s12532-019-00159-1</identifier>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Michael Winkler</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-56</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>primal heuristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fix-and-propagate</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>large neighborhood search</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain propagation</value>
    </subject>
    <collection role="msc" number="90C10">Integer programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C59">Approximation methods and heuristics</collection>
    <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="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="michael.winkler">Winkler, Michael</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="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6538/ZR-17-56.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/6538/ZR-17-56-revised.pdf</file>
  </doc>
  <doc>
    <id>5668</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>367</pageFirst>
    <pageLast>398</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>7</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Progress in presolving for mixed integer programming</title>
    <abstract language="eng">This paper describes three presolving techniques for solving mixed integer programming problems (MIPs) that were implemented in the academic MIP solver SCIP. The task of presolving is to reduce the problem size and strengthen the formulation, mainly by eliminating redundant information and exploiting problem structures. The first method fixes continuous singleton columns and extends results known from duality fixing. The second analyzes and exploits pairwise dominance relations between variables, whereas the third detects isolated subproblems and solves them independently. The performance of the presented techniques is demonstrated on two MIP test sets. One contains all benchmark instances from the last three MIPLIB versions, while the other consists of real-world supply chain management problems. The computational results show that the combination of all three presolving techniques almost halves the solving time for the considered supply chain management problems. For the MIPLIB instances we obtain a speedup of 20 % on affected instances while not degrading the performance on the remaining problems.</abstract>
    <parentTitle language="eng">Mathematical Programming Computation</parentTitle>
    <identifier type="doi">10.1007/s12532-015-0083-5</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-42530</enrichment>
    <submitter>Bettina Kasse</submitter>
    <author>Gerald Gamrath</author>
    <author>Thorsten Koch</author>
    <author>Alexander Martin</author>
    <author>Matthias Miltenberger</author>
    <author>Dieter Weninger</author>
    <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="koch">Koch, Thorsten</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>8530</id>
    <completedYear/>
    <publishedYear/>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>reportzib</type>
    <publisherName/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-12-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    </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</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>
    <collection role="msc" number="65Y05">Parallel computation</collection>
    <collection role="msc" number="90C05">Linear programming</collection>
    <collection role="msc" number="90C10">Integer programming</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="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="koch">Koch, Thorsten</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="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="schloesser">Schlösser, Franziska</collection>
    <collection role="persons" number="turner">Turner, Mark Ruben</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
    <collection role="projects" number="HPO-NAVI">HPO-NAVI</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="persons" number="hoen">Hoen, Alexander</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/8530/scipopt-80.pdf</file>
  </doc>
  <doc>
    <id>9405</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>21</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>49</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <parentTitle language="eng">ACM Transactions on Mathematical Software</parentTitle>
    <identifier type="doi">10.1145/3585516</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Ksenia Bestuzheva</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Mathieu Besançon</author>
    <author>Wei-Kun Chen</author>
    <author>Antonia Chmiela</author>
    <author>Tim Donkiewicz</author>
    <author>Jasper 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 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 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>
    <collection role="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="koch">Koch, Thorsten</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="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="schloesser">Schlösser, Franziska</collection>
    <collection role="persons" number="turner">Turner, Mark Ruben</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
    <collection role="persons" number="sofranac">Šofranac, Boro</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="hoen">Hoen, Alexander</collection>
  </doc>
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