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  <doc>
    <id>6836</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>2018-04-10</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Feature-Based Algorithm Selection for Mixed Integer Programming</title>
    <abstract language="eng">Mixed integer programming is a versatile and valuable optimization tool. However, solving specific problem instances can be computationally demanding even for cutting-edge solvers. Such long running times are often significantly reduced by an appropriate change of the solver's parameters. In this paper we investigate "algorithm selection", the task of choosing among a set of algorithms the ones that are likely to perform best for a particular instance. &#13;
&#13;
In our case, we treat different parameter settings of the MIP solver SCIP as different algorithms to choose from. Two peculiarities of the MIP solving process have our special attention. We address the well-known problem of performance variability by using multiple random seeds. Besides solving time, primal dual integrals are recorded as a second performance measure in order to distinguish solvers that timed out.&#13;
&#13;
We collected feature and performance data for a large set of publicly available MIP instances. The algorithm selection problem is addressed by several popular, feature-based methods, which have been partly extended for our purpose. Finally, an analysis of the feature space and performance results of the selected algorithms are presented.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-68362</identifier>
    <author>Alexander Georges</author>
    <submitter>Gregor Hendel</submitter>
    <author>Ambros Gleixner</author>
    <author>Gorana Gojic</author>
    <author>Robert Lion Gottwald</author>
    <author>David Haley</author>
    <author>Gregor Hendel</author>
    <author>Bartlomiej Matejczyk</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-17</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>algorithm selection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer programming</value>
    </subject>
    <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="robert.gottwald">Gottwald, Robert</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="projects" number="BEAM-ME">BEAM-ME</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/6836/main.pdf</file>
  </doc>
  <doc>
    <id>7220</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>2019-02-06</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Conflict-Driven Heuristics for Mixed Integer Programming</title>
    <abstract language="eng">Two essential ingredients of modern mixed-integer programming (MIP) solvers are diving heuristics that simulate a partial depth-first search in a branch-and-bound search tree and conflict analysis of infeasible subproblems to learn valid constraints. So far, these techniques have mostly been studied independently: primal heuristics under the aspect of finding high-quality feasible solutions early during the solving process and conflict analysis for fathoming nodes of the search tree and improving the dual bound. Here, we combine both concepts in two different ways. First, we develop a diving heuristic that targets the generation of valid conflict constraints from the Farkas dual. We show that in the primal this is equivalent to the optimistic strategy of diving towards the best bound with respect to the objective function. Secondly, we use information derived from conflict analysis to enhance the search of a diving heuristic akin to classical coefficient diving. The computational performance of both methods is evaluated using an implementation in the source-open MIP solver SCIP. Experiments are carried out on publicly available test sets including Miplib 2010 and Cor@l.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-72204</identifier>
    <identifier type="doi">10.1287/ijoc.2020.0973</identifier>
    <author>Jakob Witzig</author>
    <submitter>Jakob Witzig</submitter>
    <author>Ambros Gleixner</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-08</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer programming; primal heuristics; conflict analysis; branch-and-bound</value>
    </subject>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</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="projects" number="plan4res">Plan4res</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7220/supplement.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/7220/ZR-19-08.pdf</file>
  </doc>
  <doc>
    <id>6956</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2018-07-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Algorithmic Behavior for Solving Mixed Integer Programs Using Bandit Algorithms</title>
    <abstract language="eng">State-of-the-art solvers for mixed integer programs (MIP) govern a variety of algorithmic components. Ideally, the solver adaptively learns to concentrate its computational budget on those components that perform well on a particular problem, especially if they are time consuming.&#13;
We focus on three such algorithms, namely the classes of large neighborhood search and diving heuristics as well as Simplex pricing strategies.&#13;
For each class we propose a selection strategy that is updated based on the observed runtime behavior, aiming to ultimately select only the best algorithms for a given instance.&#13;
We review several common strategies for such a selection scenario under uncertainty, also known as Multi Armed Bandit Problem.&#13;
In order to apply those bandit strategies, we carefully design reward functions to rank and compare each individual heuristic or pricing algorithm within its respective class.&#13;
Finally, we discuss the computational benefits of using the proposed adaptive selection within the \scip Optimization Suite on publicly available MIP instances.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-69563</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Gregor Hendel</author>
    <submitter>Gregor Hendel</submitter>
    <author>Matthias Miltenberger</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-36</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>multi armed bandit</value>
    </subject>
    <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="hendel">Hendel, Gregor</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-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/6956/paper.pdf</file>
  </doc>
  <doc>
    <id>4253</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-09-23</completedDate>
    <publishedDate>2013-09-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Progress in Presolving for Mixed Integer Programming</title>
    <abstract language="eng">Presolving attempts to eliminate redundant information from the problem formulation and simultaneously tries to strengthen the formulation. It can be very effective and is often essential for solving instances. Especially for mixed integer programming problems, fast and effective presolving algorithms are very important. In this paper, we report on three new presolving techniques. The first method searches for singleton continuous columns and tries to fix the corresponding variables. Then we present a presolving technique which exploits a partial order of the variables to induce fixings. Finally, we show an approach based on connected components in graphs. Our computational results confirm the profitable use of the algorithms in practice.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-42530</identifier>
    <identifier type="doi">10.1007/s12532-015-0083-5</identifier>
    <enrichment key="SourceTitle">Appeared in: Mathematical Programming Computation, vol. 7 iss. 4 (2015),  pp. 367-398</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-42530</enrichment>
    <submitter>Gerald Gamrath</submitter>
    <author>Gerald Gamrath</author>
    <author>Thorsten Koch</author>
    <author>Alexander Martin</author>
    <author>Matthias Miltenberger</author>
    <author>Dieter Weninger</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-48</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>presolving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>preprocessing</value>
    </subject>
    <collection role="ccs" number="G.4">MATHEMATICAL SOFTWARE</collection>
    <collection role="msc" number="90-04">Explicit machine computation and programs (not the theory of computation or programming)</collection>
    <collection role="msc" number="90-08">Computational methods</collection>
    <collection role="msc" number="90C11">Mixed integer 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="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>
    <file>https://opus4.kobv.de/opus4-zib/files/4253/ZR-13-48.pdf</file>
  </doc>
  <doc>
    <id>6108</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>2016-11-22</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>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61087</identifier>
    <identifier type="doi">10.1007/978-3-319-59776-8_17</identifier>
    <enrichment key="SourceTitle">Experiments with Conflict Analysis in Mixed Integer Programming, Proceedings of CPAIOR 2017</enrichment>
    <author>Jakob Witzig</author>
    <submitter>Jakob Witzig</submitter>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-63</number>
    </series>
    <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="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>
    <file>https://opus4.kobv.de/opus4-zib/files/6108/conflict_analysis_witzig_berthold_heinz_report_16-63.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/6108/ZR-16-63-revised.pdf</file>
  </doc>
  <doc>
    <id>6104</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>2016-11-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Verifying Integer Programming Results</title>
    <abstract language="eng">Software for mixed-integer linear programming can return incorrect results for a number of reasons, one being the use of inexact floating-point arithmetic. Even solvers that employ exact arithmetic may suffer from programming or algorithmic errors, motivating the desire for a way to produce independently verifiable certificates of claimed results. Due to the complex nature of state-of-the-art MILP solution algorithms, the ideal form of such a certificate is not entirely clear. This paper proposes such a certificate format, illustrating its capabilities and structure through examples. The certificate format is designed with simplicity in mind and is composed of a list of statements that can be sequentially verified using a limited number of simple yet powerful inference rules. We present a supplementary verification tool for compressing and checking these certificates independently of how they were created.  We report computational results on a selection of mixed-integer linear programming instances from the literature.  To this end, we have extended the exact rational version of the MIP solver SCIP to produce such certificates.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61044</identifier>
    <identifier type="doi">10.1007/978-3-319-59250-3_13</identifier>
    <enrichment key="SourceTitle">F. Eisenbrand and J. Koenemann, eds., Integer Programming and Combinatorial Optimization: 19th International Conference, IPCO 2017</enrichment>
    <author>Kevin K. H. Cheung</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Ambros Gleixner</author>
    <author>Daniel Steffy</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-58</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>correctness, verification, proof, certificate, optimality, infeasibility, mixed-integer linear programming</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</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/6104/ZR-16-58.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/6104/ZR-16-58-revised.pdf</file>
  </doc>
  <doc>
    <id>6936</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>2018-07-02</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 6.0</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 6.0 of the SCIP Optimization Suite. Besides performance improvements of the MIP and MINLP core achieved by new primal heuristics and a new selection criterion for cutting planes, one focus of this release are decomposition algorithms. Both SCIP and the automatic decomposition solver GCG now include advanced functionality for performing Benders’ decomposition in a generic framework. GCG’s detection loop for structured matrices and the coordination of pricing routines for Dantzig-Wolfe decomposition has been significantly revised for greater flexibility. Two SCIP extensions have been added&#13;
to solve the recursive circle packing problem by a problem-specific column generation scheme and to demonstrate the use of the new Benders’ framework for stochastic capacitated facility location. Last, not least, the report presents updates and additions to the other components and extensions of the SCIP Optimization Suite: the LP solver SoPlex, the modeling language Zimpl, the parallelization framework UG, the Steiner tree solver SCIP-Jack, and the mixed-integer semidefinite programming solver SCIP-SDP.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-69361</identifier>
    <submitter>Matthias Miltenberger</submitter>
    <author>Ambros Gleixner</author>
    <author>Michael Bastubbe</author>
    <author>Leon Eifler</author>
    <author>Tristan Gally</author>
    <author>Gerald Gamrath</author>
    <author>Robert Lion Gottwald</author>
    <author>Gregor Hendel</author>
    <author>Christopher Hojny</author>
    <author>Thorsten Koch</author>
    <author>Marco Lübbecke</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>Franziska Schlösser</author>
    <author>Christoph Schubert</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Jan Merlin Viernickel</author>
    <author>Matthias Walter</author>
    <author>Fabian Wegscheider</author>
    <author>Jonas T. Witt</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-26</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>
      <value>Steiner tree optimization</value>
    </subject>
    <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="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>
    <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="projects" number="plan4res">Plan4res</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6936/scipopt-60.pdf</file>
  </doc>
  <doc>
    <id>4254</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-09-23</completedDate>
    <publishedDate>2013-09-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving strong branching by domain propagation</title>
    <abstract language="eng">One of the essential components of a branch-and-bound based mixed-integer linear programming (MIP) solver is the branching rule. Strong branching is a method used by many state-of-the-art branching rules to select the variable to branch on. It precomputes the dual bounds of potential child nodes by solving auxiliary linear programs (LPs) and thereby helps to take good branching decisions that lead to a small search tree. In this paper, we describe how these dual bound predictions can be improved by including domain propagation into strong branching. Domain propagation is a technique usually used at every node of the branch-and-bound tree to tighten the local domains of variables. Computational experiments on standard MIP instances indicate that our improved strong branching method significantly improves the quality of the predictions and causes almost no additional effort. For a full strong branching rule, we are able to obtain substantial reductions of the branch-and-bound tree size as well as the solving time. Moreover, also the state-of-the-art hybrid branching rule can be improved this way.&#13;
This paper extends previous work by the author published in the proceedings of the CPAIOR 2013.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-42546</identifier>
    <identifier type="doi">10.1007/s13675-014-0021-8</identifier>
    <enrichment key="SourceTitle">EURO Journal on Computational Optimization</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <series>
      <title>ZIB-Report</title>
      <number>13-47</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-bound</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branching rule</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>variable selection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>strong branching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain propagation</value>
    </subject>
    <collection role="ccs" number="G.4">MATHEMATICAL SOFTWARE</collection>
    <collection role="msc" number="90-08">Computational methods</collection>
    <collection role="msc" number="90B40">Search theory</collection>
    <collection role="msc" number="90C10">Integer programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</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="gamrath">Gamrath, Gerald</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/4254/ZR-13-47.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/4254/ZR-13-47-RevisedVersion.pdf</file>
  </doc>
  <doc>
    <id>5382</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>2015-03-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Branching on multi-aggregated variables</title>
    <abstract language="eng">In mixed-integer programming, the branching rule is a key component to a fast convergence of the branch-and-bound algorithm. The most common strategy is to branch on simple disjunctions that split the domain of a single integer variable into two disjoint intervals. Multi-aggregation is a presolving step that replaces variables by an affine linear sum of other variables, thereby reducing the problem size. While this simplification typically improves the performance of MIP solvers, it also restricts the degree of freedom in variable-based branching rules.&#13;
We present a novel branching scheme that tries to overcome the above drawback by considering general disjunctions defined by multi-aggregated variables in addition to the standard disjunctions based on single variables. This natural idea results in a hybrid between variable- and constraint-based branching rules. Our implementation within the constraint integer programming framework SCIP incorporates this into a full strong branching rule and reduces the number of branch-and-bound nodes on a general test set of publicly available benchmark instances. For a specific class of problems, we show that the solving time decreases significantly.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-53829</identifier>
    <identifier type="doi">10.1007/978-3-319-18008-3_10</identifier>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-53829</enrichment>
    <enrichment key="SourceTitle">Integration of AI and OR Techniques in Constraint Programming</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <author>Anna Melchiori</author>
    <author>Timo Berthold</author>
    <author>Ambros Gleixner</author>
    <author>Domenico Salvagnin</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-10</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming, branch-and-bound, branching rule, strong branching</value>
    </subject>
    <collection role="msc" number="90C10">Integer programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</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="berthold">Berthold, Timo</collection>
    <collection role="persons" number="gamrath">Gamrath, Gerald</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/5382/ZR-15-10.pdf</file>
  </doc>
  <doc>
    <id>5437</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2015-09-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Reoptimization Techniques in MIP Solvers</title>
    <abstract language="eng">Recently, there have been many successful applications of optimization algorithms that solve a sequence of quite similar mixed-integer programs (MIPs) as subproblems. Traditionally, each problem in the sequence is solved from scratch. In this paper we consider reoptimization techniques that try to benefit from information obtained by solving previous problems of the sequence. We focus on the case that subsequent MIPs differ only in the objective function or that the feasible region is reduced. We propose extensions of the very complex branch-and-bound algorithms employed by general MIP solvers based on the idea to ``warmstart'' using the final search frontier of the preceding solver run. We extend the academic MIP solver SCIP by these techniques to obtain a reoptimizing branch-and-bound solver and report computational results which show the effectiveness of the approach.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-54375</identifier>
    <identifier type="doi">10.1007/978-3-319-20086-6_14</identifier>
    <enrichment key="SourceTitle">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-54375</enrichment>
    <author>Jakob Witzig</author>
    <submitter>Jakob Witzig</submitter>
    <author>Gerald Gamrath</author>
    <author>Benjamin Hiller</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-24</number>
    </series>
    <collection role="ccs" number="J.">Computer Applications</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="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="TRR154-A04">TRR154-A04</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/5437/zibreport.pdf</file>
  </doc>
  <doc>
    <id>6217</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>2017-08-03</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 4.0</title>
    <abstract language="eng">The SCIP Optimization Suite is a powerful collection of optimization software that consists of the branch-cut-and-price framework and mixed-integer programming solver SCIP, the linear programming solver SoPlex, the modeling language Zimpl, the parallelization framework UG, and the generic branch-cut-and-price solver GCG. Additionally, it features the extensions SCIP-Jack for solving Steiner tree problems, PolySCIP for solving multi-objective problems, and SCIP-SDP for solving mixed-integer semidefinite programs. The SCIP Optimization Suite has been continuously developed and has now reached version 4.0. The goal of this report is to present the recent changes to the collection. We not only describe the theoretical basis, but focus on implementation aspects and their computational consequences.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-62170</identifier>
    <author>Stephen J. Maher</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Tobias Fischer</author>
    <author>Tristan Gally</author>
    <author>Gerald Gamrath</author>
    <author>Ambros Gleixner</author>
    <author>Robert Lion Gottwald</author>
    <author>Gregor Hendel</author>
    <author>Thorsten Koch</author>
    <author>Marco Lübbecke</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>Dieter Weninger</author>
    <author>Jonas T. Witt</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-12</number>
    </series>
    <collection role="msc" number="65Yxx">Computer aspects of numerical algorithms</collection>
    <collection role="msc" number="90C05">Linear programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C26">Nonconvex programming, global optimization</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="rehfeldt">Rehfeldt, Daniel</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</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/6217/scipoptsuite-40.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/6217/scipoptsuite-401.pdf</file>
  </doc>
  <doc>
    <id>5767</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>2016-02-26</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    </series>
    <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>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MINLP solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LP solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simplex method</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallel branch-and-bound</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-cut-and-price framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>generic column generation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Steiner tree solver</value>
    </subject>
    <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>
    </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="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="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="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="CRC1026">CRC1026</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/5767/scipopt-32.pdf</file>
  </doc>
  <doc>
    <id>1759</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-01-04</completedDate>
    <publishedDate>2013-01-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Shift-And-Propagate</title>
    <abstract language="eng">For mixed integer programming, recent years have seen a growing interest in the design of general purpose primal heuristics for use inside complete solvers. Many of these heuristics rely on an optimal LP solution. Finding this may itself take a significant amount of time.&#13;
&#13;
The presented paper addresses this issue by the introduction of the Shift-And-Propagate heuristic. Shift-And-Propagate is a pre-root primal heuristic that does not require a previously found LP solution. It applies domain propagation techniques to quickly drive a variable assignment towards feasibility. Computational experiments indicate that this heuristic is a powerful supplement of existing rounding and propagation heuristics.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-17596</identifier>
    <identifier type="doi">10.1007/s10732-014-9271-0</identifier>
    <enrichment key="SourceTitle">Appeared in: Journal of Heuristics 21 (2015) pp. 73-106</enrichment>
    <author>Timo Berthold</author>
    <submitter>Timo Berthold</submitter>
    <author>Gregor Hendel</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-03</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>primal heuristic</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain propagation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>rounding</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="hendel">Hendel, Gregor</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>
    <file>https://opus4.kobv.de/opus4-zib/files/1759/ZR-13-03.pdf</file>
  </doc>
  <doc>
    <id>1770</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2012-12-21</completedDate>
    <publishedDate>2012-12-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving strong branching by propagation</title>
    <abstract language="eng">Strong branching is an important component of most variable selection rules in branch-and-bound based mixed-integer linear programming solvers.&#13;
It predicts the dual bounds of potential child nodes by solving auxiliary LPs and thereby helps to keep the branch-and-bound tree small.&#13;
In this paper, we describe how these dual bound predictions can be improved by including domain propagation into strong branching.&#13;
Computational experiments on standard MIP instances indicate that this is beneficial in three aspects: It helps to reduce the average number of LP iterations per strong branching call, the number of branch-and-bound nodes, and the overall solving time.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-17701</identifier>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <series>
      <title>ZIB-Report</title>
      <number>12-46</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-bound</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branching rule</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>strong branching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain propagation</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1770/ZR-12-46.pdf</file>
  </doc>
  <doc>
    <id>6110</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>2016-11-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming</title>
    <abstract language="eng">SAP's decision support systems for optimized supply network planning rely on mixed-integer programming as the core engine to compute optimal or near-optimal solutions. The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of a robust and future-proof decision support system for a large and diverse customer base. In this paper we describe our coordinated efforts to ensure that the performance of the underlying solution algorithms matches the complexity of the large supply chain problems and tight time limits encountered in practice.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61107</identifier>
    <identifier type="doi">10.4208/jcm.1905-m2019-0055</identifier>
    <enrichment key="SourceTitle">Journal of Computational Mathematics</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Ambros Gleixner</author>
    <author>Thorsten Koch</author>
    <author>Matthias Miltenberger</author>
    <author>Dimitri Kniasew</author>
    <author>Dominik Schlögel</author>
    <author>Alexander Martin</author>
    <author>Dieter Weninger</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-45</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>supply chain management, supply network optimization, mixed-integer linear programming, primal heuristics, numerical stability, large-scale optimization</value>
    </subject>
    <collection role="msc" number="90B06">Transportation, logistics</collection>
    <collection role="msc" number="90C11">Mixed integer 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="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-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/6110/ZR-16-45.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/6110/ZR-16-45-rev1.pdf</file>
  </doc>
  <doc>
    <id>6138</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>2016-12-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Distributed domain propagation</title>
    <abstract language="eng">Portfolio parallelization is an approach that runs several solver instances in parallel and terminates when one of them succeeds in solving the problem. Despite it's simplicity portfolio parallelization has been shown to perform well for modern mixed-integer programming (MIP) and boolean satisfiability problem (SAT) solvers. Domain propagation has also been shown to be a simple technique in modern MIP and SAT solvers that effectively finds additional domain reductions after a variables domain has been reduced. This paper investigates the impact of distributed domain propagation in modern MIP solvers that employ portfolio parallelization. Computational experiments were conducted for two implementations of this parallelization approach. While both share global variable bounds and solutions they communicate differently. In one implementation the communication is performed only at designated points in the solving process and in the other it is performed completely asynchronously. Computational experiments show a positive performance impact of communicating global variable bounds and provide valuable insights in communication strategies for parallel solvers.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61380</identifier>
    <identifier type="doi">10.4230/LIPIcs.SEA.2017.6</identifier>
    <enrichment key="SourceTitle">16th International Symposium on Experimental Algorithms (SEA 2017). Leibniz International Proceedings in Informatics, Volume 75, SEA 2017, June 21–23, 2017 - London, UK Costas S. Iliopoulos and Solon P. Pissis and Simon J. Puglisi and Rajeev Raman (Eds.)</enrichment>
    <author>Robert Lion Gottwald</author>
    <submitter>Robert Lion Gottwald</submitter>
    <author>Stephen J. Maher</author>
    <author>Yuji Shinano</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-71</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallelization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain propagation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>portfolio solvers</value>
    </subject>
    <collection role="ccs" number="G.">Mathematics of Computing</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="robert.gottwald">Gottwald, Robert</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</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/6138/16-71.pdf</file>
  </doc>
  <doc>
    <id>5459</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>2015-04-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Enhancing MIP branching decisions by using the sample variance of pseudo-costs</title>
    <abstract language="eng">The  selection of a good branching variable is crucial for small search trees in Mixed Integer Programming. Most modern solvers employ a strategy guided by history information, mainly the variable pseudo-costs, which are used to estimate the objective gain. At the beginning&#13;
of the search, such information is usually collected via an expensive look-ahead strategy called strong-branching until variables are considered reliable.&#13;
The reliability notion is thereby mostly based on fixed-number thresholds, which may lead to ineffective branching decisions on problems with highly varying objective gains.&#13;
&#13;
We suggest two new notions of reliability motivated by mathematical statistics that take into account the sample variance of the past observations on each variable individually. The first method prioritizes additional strong-branching look-aheads on variables whose pseudo-costs&#13;
show a large variance by measuring the relative error of a pseudo-cost confidence interval. The second method performs a two-sample Student-t test for filtering branching candidates with a high probability to be better than the best history candidate.&#13;
&#13;
Both methods were implemented in the MIP-solver SCIP and computational results on standard MIP test sets are presented.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="doi">10.1007/978-3-319-18008-3_14</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-54591</identifier>
    <enrichment key="SourceTitle">Appeared in: Integration of AI and OR Techniques in Constraint Programming. Lecture Notes in Computer Science 9075. 2015 pp. 199-214</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-54591</enrichment>
    <author>Gregor Hendel</author>
    <submitter>Gregor Hendel</submitter>
    <series>
      <title>ZIB-Report</title>
      <number>15-28</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reliability branching</value>
    </subject>
    <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="hendel">Hendel, Gregor</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>
    <file>https://opus4.kobv.de/opus4-zib/files/5459/paper.pdf</file>
  </doc>
  <doc>
    <id>6472</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>2017-07-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Measuring the impact of branching rules for mixed-integer programming</title>
    <abstract language="eng">Branching rules are an integral component of the branch-and-bound algorithm typically used to solve mixed-integer programs and subject to intense research. Different approaches for branching are typically compared based on the solving time as well as the size of the branch-and-bound tree needed to prove optimality. The latter, however, has some flaws when it comes to sophisticated branching rules that do not only try to take a good branching decision, but have additional side-effects. We propose a new measure for the quality of a branching rule that distinguishes tree size reductions obtained by better branching decisions from those obtained by such side-effects. It is evaluated for common branching rules providing new insights in the importance of strong branching.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-64722</identifier>
    <identifier type="doi">10.1007/978-3-319-89920-6_23</identifier>
    <enrichment key="SourceTitle">Appeared in: Operations Reseach Proceedings 2017 (2018) pp 165-170</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <author>Christoph Schubert</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-34</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-bound</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branching rule</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>strong branching</value>
    </subject>
    <collection role="msc" number="90-08">Computational methods</collection>
    <collection role="msc" number="90B40">Search theory</collection>
    <collection role="msc" number="90C10">Integer programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</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="gamrath">Gamrath, Gerald</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>
    <file>https://opus4.kobv.de/opus4-zib/files/6472/ZR-17-34-rev1.pdf</file>
  </doc>
  <doc>
    <id>6464</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>2017-07-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploring the Numerics of Branch-and-Cut for Mixed Integer Linear Optimization</title>
    <abstract language="eng">We investigate how the numerical properties of the LP relaxations evolve&#13;
throughout the solution procedure in a solver employing the branch-and-cut&#13;
algorithm. The long-term goal of this work is to determine whether the effect&#13;
on the numerical conditioning of the LP relaxations resulting from the&#13;
branching and cutting operations can be effectively predicted&#13;
and whether such predictions can be used to make better algorithmic&#13;
choices. In a first step towards this goal, we discuss here the numerical&#13;
behavior of an existing solver in order to determine whether our &#13;
intuitive understanding of this behavior is correct.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2017</parentTitle>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-64645</identifier>
    <author>Matthias Miltenberger</author>
    <submitter>Matthias Miltenberger</submitter>
    <author>Ted Ralphs</author>
    <author>Daniel Steffy</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-43</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed Integer Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Algorithm Analysis</value>
    </subject>
    <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="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-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/6464/ZR-17-43-published.pdf</file>
  </doc>
  <doc>
    <id>6611</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>2017-12-13</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Conflict Driven Diving for Mixed Integer Programming</title>
    <abstract language="eng">The analysis of infeasibility plays an important role in solving satisfiability problems (SAT) and mixed integer programs (MIPs). In mixed integer programming, this procedure is called conflict analysis. So far, modern MIP solvers use conflict analysis only for propagation and improving the dual bound, i.e., fathoming nodes that cannot contain feasible solutions. In this short paper, we present a new approach which uses conflict information to improve the primal bound during a MIP solve. To derive new improving primal solutions we use a conflict driven diving heuristic called conflict diving that uses the information obtained by conflict analysis. Conflict diving pursues a twofold strategy. By using conflict information the new diving approach is guided into parts of the search space that are usually not explored by other diving heuristics. At the same time, conflict diving has a fail-fast-strategy to reduce the time spent if it cannot find a new primal solution. As a byproduct, additional valid conflict constraints can be derived, from which a MIP solver can gain benefit to improve the dual bound as well. To show the added-value of conflict diving within a MIP solver, conflict diving has been implemented within the non-commercial MIP solver SCIP. Experiments are carried out on general MIP instances from standard public test sets, like MIPLIB2010 or Cor@l.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-66116</identifier>
    <author>Jakob Witzig</author>
    <submitter>Jakob Witzig</submitter>
    <series>
      <title>ZIB-Report</title>
      <number>17-69</number>
    </series>
    <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="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>
    <file>https://opus4.kobv.de/opus4-zib/files/6611/ZR-17-49-revised.pdf</file>
  </doc>
  <doc>
    <id>1325</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2011-07-14</completedDate>
    <publishedDate>2011-07-14</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Rounding and Propagation Heuristics for Mixed Integer Programming</title>
    <abstract language="eng">Primal heuristics are an important component of state-of-the-art codes for&#13;
mixed integer programming. In this paper, we focus on primal heuristics&#13;
that only employ computationally inexpensive procedures such as rounding&#13;
and logical deductions (propagation). We give an overview of eight&#13;
different approaches. To assess the impact of these primal  heuristics on&#13;
the ability to find feasible solutions, in particular early during search,&#13;
we introduce a new performance measure, the primal integral. Computational&#13;
experiments evaluate this and other measures on MIPLIB~2010 benchmark&#13;
instances.</abstract>
    <identifier type="serial">11-29</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-13255</identifier>
    <identifier type="doi">10.1007/978-3-642-29210-1_12</identifier>
    <enrichment key="SourceTitle">Appeared in: Operations Research Proceedings 2011. Diethard Klatte et al. (eds.) Springer 2012, pp. 71-76</enrichment>
    <author>Tobias Achterberg</author>
    <submitter>Timo Berthold</submitter>
    <author>Timo Berthold</author>
    <author>Gregor Hendel</author>
    <series>
      <title>ZIB-Report</title>
      <number>11-29</number>
    </series>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>primal heuristic</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>mixed integer programming</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>domain propagation</value>
    </subject>
    <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="achterberg">Achterberg, Tobias</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1325/ZR-11-29.pdf</file>
  </doc>
  <doc>
    <id>6134</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>2016-01-12</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite</title>
    <abstract language="eng">SCIP is a solver for a wide variety of mathematical optimization problems. It is written in C and extendable due to its plug-in based design. However, dealing with all C specifics when extending SCIP can be detrimental to development and testing of new ideas. This paper attempts to provide a remedy by introducing PySCIPOpt, a Python interface to SCIP that enables users to write new SCIP code entirely in Python. We demonstrate how to intuitively model mixed-integer linear and quadratic optimization problems and moreover provide examples on how new Python plug-ins can be added to SCIP.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61348</identifier>
    <identifier type="doi">10.1007/978-3-319-42432-3_37</identifier>
    <enrichment key="SourceTitle">Appeared in: Mathematical Software – ICMS 2016, Volume 9725, Pages 301-307</enrichment>
    <author>Stephen J. Maher</author>
    <submitter>Matthias Miltenberger</submitter>
    <author>Matthias Miltenberger</author>
    <author>João Pedro Pedroso</author>
    <author>Daniel Rehfeldt</author>
    <author>Robert Schwarz</author>
    <author>Felipe Serrano</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-64</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SCIP, Mathematical optimization, Python, Modeling</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</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="miltenberger">Miltenberger, Matthias</collection>
    <collection role="persons" number="rehfeldt">Rehfeldt, Daniel</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="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6134/PySCIPOpt.pdf</file>
  </doc>
  <doc>
    <id>5229</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>2014-09-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">SCIP-Jack - A massively parallel STP solver</title>
    <abstract language="eng">In this article we describe the impact from embedding a 15 year old model for solving the Steiner tree problem in graphs in a state-of-the-art MIP-Framework, making the result run in a massively parallel environment and extending the model to solve as many variants as possible. We end up with a high-perfomance solver that is capable of solving previously unsolved instances and, in contrast to its predecessor, is freely available for academic research.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-52293</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Gerald Gamrath</submitter>
    <author>Thorsten Koch</author>
    <author>Daniel Rehfeldt</author>
    <author>Yuji Shinano</author>
    <series>
      <title>ZIB-Report</title>
      <number>14-35</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Steiner Tree Problem</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>DIMACS Challenge</value>
    </subject>
    <collection role="ccs" number="">Trees</collection>
    <collection role="ccs" number="">Algorithm design and analysis (REVISED)</collection>
    <collection role="msc" number="90C27">Combinatorial optimization</collection>
    <collection role="msc" number="90C57">Polyhedral combinatorics, branch-and-bound, branch-and-cut</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="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="rehfeldt">Rehfeldt, Daniel</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</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>
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  </doc>
  <doc>
    <id>5709</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2015-12-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploiting Solving Phases for Mixed-Integer Programs</title>
    <abstract language="eng">Modern MIP solving software&#13;
incorporates dozens of auxiliary algorithmic components for supporting&#13;
the branch-and-bound search in finding and improving solutions and in strengthening the relaxation.&#13;
Intuitively, a dynamic solving strategy with an appropriate emphasis on different solving components and strategies is desirable during the search process.&#13;
We propose an adaptive solver behavior that dynamically reacts &#13;
on transitions between the three typical phases of a MIP solving process:&#13;
The first phase objective is to find a feasible solution. During the second phase,&#13;
a sequence of incumbent solutions gets constructed&#13;
until the incumbent is eventually optimal. Proving&#13;
optimality is the central objective of the remaining third phase.&#13;
Based on the MIP-solver SCIP, we demonstrate &#13;
the usefulness of the phase concept both with an exact recognition of the optimality of a solution, and provide&#13;
heuristic alternatives to make use of the concept in practice.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-57098</identifier>
    <identifier type="doi">10.1007/978-3-319-42902-1_1</identifier>
    <enrichment key="SourceTitle">Appeared in: Operations Research Proceedings 2015</enrichment>
    <author>Gregor Hendel</author>
    <submitter>Gregor Hendel</submitter>
    <series>
      <title>ZIB-Report</title>
      <number>15-64</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-Bound, Mixed-Integer Programming</value>
    </subject>
    <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="hendel">Hendel, Gregor</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>
    <file>https://opus4.kobv.de/opus4-zib/files/5709/Hendel_ExploitingSolvingPhases.pdf</file>
  </doc>
  <doc>
    <id>5511</id>
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    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2015-05-28</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Iterative Refinement for Linear Programming</title>
    <abstract language="eng">We describe an iterative refinement procedure for computing extended precision or exact solutions to linear programming problems (LPs).  Arbitrarily precise solutions can be computed by solving a sequence of closely related LPs with limited precision arithmetic.  The LPs solved&#13;
share the same constraint matrix as the original problem instance and are transformed only by modification of the objective function, right-hand side, and variable bounds. Exact computation is used to compute and store the exact representation of the transformed problems, while numeric computation is used for solving LPs.  At all steps of the algorithm the LP bases encountered in the transformed problems correspond directly to LP bases in the original problem description. We show that this algorithm is effective in practice for computing extended precision solutions and that it leads to a direct improvement of the best known methods for solving LPs exactly over the rational numbers. Our implementation is publically available as an extension of the academic LP solver SoPlex.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-55118</identifier>
    <identifier type="doi">10.1287/ijoc.2016.0692</identifier>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-55118</enrichment>
    <enrichment key="SourceTitle">INFORMS Journal on Computing</enrichment>
    <author>Ambros Gleixner</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Daniel Steffy</author>
    <author>Kati Wolter</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-15</number>
    </series>
    <collection role="msc" number="90C05">Linear programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</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/5511/ZR-15-15.pdf</file>
  </doc>
  <doc>
    <id>5551</id>
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    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>reportzib</type>
    <publisherName/>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2015-07-03</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Structure-based primal heuristics for mixed integer programming</title>
    <abstract language="eng">Primal heuristics play an important role in the solving of mixed integer programs (MIPs). They help to reach optimality faster and provide good feasible solutions early in the solving process. In this paper, we present two new primal heuristics which take into account global structures available within MIP solvers to construct feasible solutions at the beginning of the solving process. These heuristics follow a large neighborhood search (LNS) approach and use global structures to define a neighborhood that is with high probability significantly easier to process while (hopefully) still containing good feasible solutions. The definition of the neighborhood is done by iteratively fixing variables and propagating these fixings. Thereby, fixings are determined based on the predicted impact they have on the subsequent domain propagation. The neighborhood is solved as a sub-MIP and solutions are transferred back to the original problem. Our computational experiments on standard MIP test sets show that the proposed heuristics find solutions for about every third instance and therewith help to improve the average solving time.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-55518</identifier>
    <identifier type="doi">http://dx.doi.org/10.1007/978-4-431-55420-2_3</identifier>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-55518</enrichment>
    <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>15-26</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer programming, large neighborhood search, primal heuristics, 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/5551/ZR-15-26.pdf</file>
  </doc>
  <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>6160</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2016-12-30</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Three Phases of MIP Solving</title>
    <abstract language="eng">Modern MIP solvers employ dozens of auxiliary algorithmic components to support the branch-and-bound search in finding and improving primal solutions and in strengthening the dual bound.&#13;
Typically, all components are tuned  to minimize the average running time to prove optimality. In this article, we take a different look at the run of a MIP solver. We argue that the solution process consists of three different phases, namely achieving feasibility, improving the incumbent solution, and proving optimality. We first show that the entire solving process can be improved by adapting the search strategy with respect to the phase-specific aims using different control tunings. Afterwards, we provide criteria to predict the transition between the individual phases and evaluate the performance impact of altering the algorithmic behavior of the MIP solver SCIP at the predicted phase transition points.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61607</identifier>
    <identifier type="doi">10.1080/10556788.2017.1392519</identifier>
    <enrichment key="SourceTitle">Optimization Methods and Software</enrichment>
    <author>Timo Berthold</author>
    <submitter>Gregor Hendel</submitter>
    <author>Gregor Hendel</author>
    <author>Thorsten Koch</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-78</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimization software</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-bound</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>adaptive search behavior</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimality prediction</value>
    </subject>
    <collection role="ccs" number="G.">Mathematics of Computing</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="berthold">Berthold, Timo</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="persons" number="koch">Koch, Thorsten</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/6160/zib-paper.pdf</file>
  </doc>
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