<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>3260</id>
    <completedYear>2008</completedYear>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>278</pageFirst>
    <pageLast>282</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>5015</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Counting Solutions of Integer Programs Using Unrestricted Subtree Detection</title>
    <parentTitle language="eng">Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 5th International Conference, CPAIOR 2008</parentTitle>
    <identifier type="url">http://opus.kobv.de/zib/volltexte/2008/1092/</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-10632</enrichment>
    <author>Tobias Achterberg</author>
    <editor>Laurent Perron</editor>
    <author>Stefan Heinz</author>
    <editor>Michael Trick</editor>
    <author>Thorsten Koch</author>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="achterberg">Achterberg, Tobias</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="projects" number="VeriCount">VeriCount</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>7192</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>84</pageFirst>
    <pageLast>94</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>11494</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-04-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Status Report on Conflict Analysis in Mixed Integer Nonlinear Programming</title>
    <abstract language="deu">Mixed integer nonlinear programs (MINLPs) are arguably among the hardest optimization problems, with a wide range of applications. MINLP solvers that are based on linear relaxations and spatial branching work similar as mixed integer programming (MIP) solvers in the sense that they are based on a branch-and-cut algorithm, enhanced by various heuristics, domain propagation, and presolving techniques. However, the analysis of infeasible subproblems, which is an important component of most major MIP solvers, has been hardly studied in the context of MINLPs. There are two main approaches for infeasibility analysis in MIP solvers: conflict graph analysis, which originates from artificial intelligence and constraint programming, and dual ray analysis.&#13;
The main contribution of this short paper is twofold. Firstly, we present the first computational study regarding the impact of dual ray analysis on convex and nonconvex MINLPs. In that context, we introduce a modified generation of infeasibility proofs that incorporates linearization cuts that are only locally valid. Secondly, we describe an extension of conflict analysis that works directly with the nonlinear relaxation of convex MINLPs instead of considering a linear relaxation. This is work-in-progress, and this short paper is meant to present first theoretical considerations without a computational study for that part.</abstract>
    <parentTitle language="eng">Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2019</parentTitle>
    <identifier type="doi">10.1007/978-3-030-19212-9_6</identifier>
    <enrichment key="AcceptedDate">2019-02-01</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="Series">LNCS</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-71170</enrichment>
    <author>Jakob Witzig</author>
    <submitter>Jakob Witzig</submitter>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</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="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>
  </doc>
  <doc>
    <id>4828</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>400</pageFirst>
    <pageLast>409</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>6630</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Explanations for the Cumulative Constraint: An Experimental Study</title>
    <parentTitle language="eng">Experimental Algorithms</parentTitle>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-12668</enrichment>
    <author>Stefan Heinz</author>
    <submitter>Sebastian Schenker</submitter>
    <author>Jens Schulz</author>
    <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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>4821</id>
    <completedYear>2012</completedYear>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst>165</pageFirst>
    <pageLast>192</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="jpn">SCIP Optimization Suite を利用した 混合整数(線形/非線形) 計画問題の解法</title>
    <abstract language="jpn">この論文ではソフトウェア・パッケージSCIP Optimization Suite を紹介し，その３つの構成要素：モデリン グ言語Zimpl, 線形計画（LP: linear programming) ソルバSoPlex, そして，制約整数計画(CIP: constraint integer programming) に対するソフトウェア・フレームワークSCIP, について述べる．本論文では，この３つの 構成要素を利用して，どのようにして挑戦的な混合整数線形計画問題(MIP: mixed integer linear optimization problems) や混合整数非線形計画問題(MINLP: mixed integer nonlinear optimization problems) をモデル化 し解くのかを説明する．SCIP は，現在，最も高速なMIP,MINLP ソルバの１つである．いくつかの例により， Zimpl, SCIP, SoPlex の利用方法を示すとともに，利用可能なインタフェースの概要を示す．最後に，将来の開 発計画の概要について述べる．</abstract>
    <abstract language="eng">This paper introduces the SCIP Optimization Suite and discusses the capabilities of its three components: the modeling language Zimpl, the linear programming solver SoPlex, and the constraint integer programming framework SCIP. We explain how in concert these can be used to model and solve challenging mixed integer linear and nonlinear optimization problems. SCIP is currently one of the fastest non-commercial MIP and MINLP solvers. We demonstrate the usage of Zimpl, SCIP, and SoPlex by selected examples, we give an overview over available interfaces, and outline plans for future development.</abstract>
    <parentTitle language="jpn">Proceedings of the 24th RAMP symposium. The Operations Society of Japan, RAMP: Research Association of Mathematical Programming</parentTitle>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-15598</enrichment>
    <author>Timo Berthold</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Ambros Gleixner</author>
    <author>Stefan Heinz</author>
    <author>Thorsten Koch</author>
    <author>Yuji Shinano</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="koch">Koch, Thorsten</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</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>
  </doc>
  <doc>
    <id>4825</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>211</pageFirst>
    <pageLast>227</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>7298</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Reconsidering Mixed Integer Programming and MIP-based Hybrids for Scheduling</title>
    <parentTitle language="eng">Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2012)</parentTitle>
    <enrichment key="Series">Lectures Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-14660</enrichment>
    <author>Stefan Heinz</author>
    <submitter>Sebastian Schenker</submitter>
    <author>J. Christopher Beck</author>
    <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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>4827</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>229</pageFirst>
    <pageLast>239</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>6595</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An approximative Criterion for the Potential of Energetic Reasoning</title>
    <parentTitle language="eng">Theory and Practice of Algorithms in (Computer) Systems</parentTitle>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-12655</enrichment>
    <author>Timo Berthold</author>
    <submitter>Sebastian Schenker</submitter>
    <author>Stefan Heinz</author>
    <author>Jens Schulz</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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>4767</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>51</pageFirst>
    <pageLast>60</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>3355</id>
    <completedYear>2006</completedYear>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>230</pageFirst>
    <pageLast>243</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3879</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Online Target Date Assignment Problem</title>
    <parentTitle language="eng">Proc. 3rd Workshop on Approximation and Online Algorithms</parentTitle>
    <identifier type="url">http://opus.kobv.de/zib/volltexte/2005/894/</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-8945</enrichment>
    <author>Stefan Heinz</author>
    <editor>Thomas Erlebach</editor>
    <author>Sven Krumke</author>
    <editor>Giuseppe Persiano</editor>
    <author>Nicole Megow</author>
    <author>Jörg Rambau</author>
    <author>Andreas Tuchscherer</author>
    <author>Tjark Vredeveld</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="projects" number="MATHEON-B14:Comb-Log">MATHEON-B14:Comb-Log</collection>
    <collection role="projects" number="MATHEON-C6:Online-Reopt">MATHEON-C6:Online-Reopt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>3270</id>
    <completedYear>2012</completedYear>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>135</pageFirst>
    <pageLast>148</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ParaSCIP: a parallel extension of SCIP</title>
    <parentTitle language="eng">Competence in High Performance Computing 2010</parentTitle>
    <identifier type="doi">10.1007/978-3-642-24025-6_12</identifier>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-11921</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Yuji Shinano</author>
    <editor>Christian Bischof</editor>
    <author>Tobias Achterberg</author>
    <editor>Heinz-Gerd Hegering</editor>
    <author>Timo Berthold</author>
    <editor>Wolfgang Nagel</editor>
    <author>Stefan Heinz</author>
    <editor>Gabriel Wittum</editor>
    <author>Thorsten Koch</author>
    <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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6088</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>308</pageFirst>
    <pageLast>316</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-07-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A First Implementation of ParaXpress: Combining Internal and External Parallelization to Solve MIPs on Supercomputers</title>
    <parentTitle language="eng">Mathematical Software - ICMS 2016, 5th International Conference Berlin, Germany, July 11-14, 2016 Proceedings</parentTitle>
    <identifier type="doi">10.1007/978-3-319-42432-3_38</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Yuji Shinano</author>
    <submitter>Julia Boltze</submitter>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</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="shinano">Shinano, Yuji</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>
  </doc>
  <doc>
    <id>6085</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>251</pageFirst>
    <pageLast>258</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-07-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Parallelization of the FICO Xpress-Optimizer</title>
    <parentTitle language="eng">Mathematical Software - ICMS 2016, 5th International Conference Berlin, Germany, July 11-14, 2016 Proceedings</parentTitle>
    <identifier type="doi">10.1007/978-3-319-42432-3_31</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Timo Berthold</author>
    <submitter>Julia Boltze</submitter>
    <author>James Farmer</author>
    <author>Stefan Heinz</author>
    <author>Michael Perregaard</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="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>
  </doc>
  <doc>
    <id>5890</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Solving Open MIP Instances with ParaSCIP on Supercomputers using up to 80,000 Cores</title>
    <abstract language="eng">This paper describes how we solved 12 previously unsolved mixed-integer program- ming (MIP) instances from the MIPLIB benchmark sets. To achieve these results we used an enhanced version of ParaSCIP, setting a new record for the largest scale MIP computation: up to 80,000 cores in parallel on the Titan supercomputer. In this paper we describe the basic parallelization mechanism of ParaSCIP, improvements of the dynamic load balancing and novel techniques to exploit the power of parallelization for MIP solving. We give a detailed overview of computing times and statistics for solving open MIPLIB instances.</abstract>
    <parentTitle language="eng">Proc. of 30th IEEE International Parallel &amp; Distributed Processing Symposium</parentTitle>
    <identifier type="doi">10.1109/IPDPS.2016.56</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-56404</enrichment>
    <author>Yuji Shinano</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Tobias Achterberg</author>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Thorsten Koch</author>
    <author>Michael Winkler</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="achterberg">Achterberg, Tobias</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</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>
  </doc>
  <doc>
    <id>2818</id>
    <completedYear>2010</completedYear>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>313</pageFirst>
    <pageLast>317</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>6140</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Constraint Integer Programming Approach for Resource-Constrained Project Scheduling</title>
    <parentTitle language="eng">Proc. of CPAIOR 2010</parentTitle>
    <enrichment key="Series">LNCS</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-11180</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Timo Berthold</author>
    <editor>Andrea Lodi</editor>
    <author>Stefan Heinz</author>
    <editor>Michela Milano</editor>
    <author>Marco Lübbecke</author>
    <editor>Paolo Toth</editor>
    <author>Rolf Möhring</author>
    <author>Jens Schulz</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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>2821</id>
    <completedYear>2009</completedYear>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>441</pageFirst>
    <pageLast>446</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>5584</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Nonlinear pseudo-Boolean optimization</title>
    <parentTitle language="eng">Theory and Applications of Satisfiability Testing – SAT 2009</parentTitle>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-11232</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Timo Berthold</author>
    <editor>Oliver Kullmann</editor>
    <author>Stefan Heinz</author>
    <author>Marc Pfetsch</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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6406</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>211</pageFirst>
    <pageLast>222</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>10335</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-05-31</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Experiments with Conflict Analysis in Mixed Integer Programming</title>
    <abstract language="eng">The analysis of infeasible subproblems plays an import role in solving mixed integer programs (MIPs) and is implemented in most major MIP solvers. There are two fundamentally different concepts to generate valid global constraints from infeasible subproblems. The first is to analyze the sequence of implications obtained by domain propagation that led to infeasibility. The result of the analysis is one or more sets of contradicting variable bounds from which so-called conflict constraints can be generated. This concept has its origin in solving satisfiability problems and is similarly used in constraint programming. The second concept is to analyze infeasible linear programming (LP) relaxations. The dual LP solution provides a set of multipliers that can be used to generate a single new globally valid linear constraint. The main contribution of this short paper is an empirical evaluation of two ways to combine both approaches. Experiments are carried out on general MIP instances from standard public test sets such as Miplib2010; the presented algorithms have been implemented within the non-commercial MIP solver SCIP. Moreover, we present a pool-based approach to manage conflicts which addresses the way a MIP solver traverses the search tree better than aging strategies known from SAT solving.</abstract>
    <parentTitle language="eng">Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2017</parentTitle>
    <identifier type="doi">10.1007/978-3-319-59776-8_17</identifier>
    <enrichment key="Series">LNCS</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-61087</enrichment>
    <enrichment key="AcceptedDate">2017-01-17</enrichment>
    <author>Jakob Witzig</author>
    <submitter>Jakob Witzig</submitter>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</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="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="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>5708</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1552</pageFirst>
    <pageLast>1561</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE Computer Society</publisherName>
    <publisherPlace>Washington, DC, USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Solving Hard MIPLIP2003 Problems with ParaSCIP on Supercomputers: An Update</title>
    <parentTitle language="deu">IPDPSW'14 Proceedings of the 2014 IEEE,  International  Parallel &amp; Distributed Processing Symposium Workshops</parentTitle>
    <identifier type="isbn">978-1-4799-4117-9</identifier>
    <identifier type="doi">10.1109/IPDPSW.2014.174</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <editor> IEEE</editor>
    <submitter>Bettina Kasse</submitter>
    <author>Yuji Shinano</author>
    <submitter>Thorsten Koch</submitter>
    <author>Tobias Achterberg</author>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Thorsten Koch</author>
    <author>Michael Winkler</author>
    <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="michael.winkler">Winkler, Michael</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
