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  <doc>
    <id>7802</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>2020-03-30</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 7.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for&#13;
mathematical optimization centered around the constraint integer programming frame-&#13;
work SCIP. This paper discusses enhancements and extensions contained in version 7.0&#13;
of the SCIP Optimization Suite. The new version features the parallel presolving library&#13;
PaPILO as a new addition to the suite. PaPILO 1.0 simplifies mixed-integer linear op-&#13;
timization problems and can be used stand-alone or integrated into SCIP via a presolver&#13;
plugin. SCIP 7.0 provides additional support for decomposition algorithms. Besides im-&#13;
provements in the Benders’ decomposition solver of SCIP, user-defined decomposition&#13;
structures can be read, which are used by the automated Benders’ decomposition solver&#13;
and two primal heuristics. Additionally, SCIP 7.0 comes with a tree size estimation&#13;
that is used to predict the completion of the overall solving process and potentially&#13;
trigger restarts. Moreover, substantial performance improvements of the MIP core were&#13;
achieved by new developments in presolving, primal heuristics, branching rules, conflict&#13;
analysis, and symmetry handling. Last, not least, the report presents updates to other&#13;
components and extensions of the SCIP Optimization Suite, in particular, the LP solver&#13;
SoPlex and the mixed-integer semidefinite programming solver SCIP-SDP.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-78023</identifier>
    <author>Gerald Gamrath</author>
    <submitter>Felipe Serrano</submitter>
    <author>Daniel Anderson</author>
    <author>Ksenia Bestuzheva</author>
    <author>Wei-Kun Chen</author>
    <author>Leon Eifler</author>
    <author>Maxime Gasse</author>
    <author>Patrick Gemander</author>
    <author>Ambros Gleixner</author>
    <author>Leona Gottwald</author>
    <author>Katrin Halbig</author>
    <author>Gregor Hendel</author>
    <author>Christopher Hojny</author>
    <author>Thorsten Koch</author>
    <author>Pierre Le Bodic</author>
    <author>Stephen J. Maher</author>
    <author>Frederic Matter</author>
    <author>Matthias Miltenberger</author>
    <author>Erik Mühmer</author>
    <author>Benjamin Müller</author>
    <author>Marc Pfetsch</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Christine Tawfik</author>
    <author>Stefan Vigerske</author>
    <author>Fabian Wegscheider</author>
    <author>Dieter Weninger</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>20-10</number>
    </series>
    <collection role="msc" number="65-XX">NUMERICAL ANALYSIS</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="robert.gottwald">Gottwald, Robert</collection>
    <collection role="persons" number="hendel">Hendel, Gregor</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="schloesser">Schlösser, Franziska</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
    <collection role="projects" number="plan4res">Plan4res</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7802/scipopt-70.pdf</file>
  </doc>
  <doc>
    <id>7275</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>2019-03-13</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Using two-dimensional Projections for Stronger Separation and Propagation of Bilinear Terms</title>
    <abstract language="eng">One of the most fundamental ingredients in mixed-integer nonlinear programming solvers is the well- known McCormick relaxation for a product of two variables x and y over a box-constrained domain. The starting point of this paper is the fact that the convex hull of the graph of xy can be much tighter when computed over a strict, non-rectangular subset of the box. In order to exploit this in practice, we propose to compute valid linear inequalities for the projection of the feasible region onto the x-y-space by solving a sequence of linear programs akin to optimization-based bound tightening. These valid inequalities allow us to employ results from the literature to strengthen the classical McCormick relaxation. As a consequence, we obtain a stronger convexification procedure that exploits problem structure and can benefit from supplementary information obtained during the branch-and bound algorithm such as an objective cutoff. We complement this by a new bound tightening procedure that efficiently computes the best possible bounds for x, y, and xy over the available projections. Our computational evaluation using the academic solver SCIP exhibit that the proposed methods are applicable to a large portion of the public test library MINLPLib and help to improve performance significantly.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-72759</identifier>
    <author>Benjamin Müller</author>
    <submitter>Benjamin Müller</submitter>
    <author>Felipe Serrano</author>
    <author>Ambros Gleixner</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-15</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer quadratically constrained programs</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nonconvex</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>separation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>propagation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>projection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bilinear terms</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="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="EnBA-M">EnBA-M</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7275/ZR-19-15.pdf</file>
  </doc>
  <doc>
    <id>5780</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>2016-03-08</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Three Enhancements for Optimization-Based Bound Tightening</title>
    <abstract language="eng">Optimization-based bound tightening (OBBT) is one of the most effective procedures to reduce variable domains of nonconvex mixed-integer nonlinear programs (MINLPs). At the same time it is one of the most expensive bound tightening procedures, since it solves auxiliary linear programs (LPs)—up to twice the number of variables many. The main goal of this paper is to discuss algorithmic techniques for an efficient implementation of OBBT.&#13;
Most state-of-the-art MINLP solvers apply some restricted version of OBBT and it seems to be common belief that OBBT is beneficial if only one is able to keep its computational cost under control. To this end, we introduce three techniques to increase the efficiency of OBBT: filtering strategies to reduce the number of solved LPs, ordering heuristics to exploit simplex warm starts, and the generation of Lagrangian variable bounds (LVBs). The propagation of LVBs during tree search is a fast approximation to OBBT without the need to solve auxiliary LPs.&#13;
We conduct extensive computational experiments on MINLPLib2. Our results indicate that OBBT is most beneficial on hard instances, for which we observe a speedup of 17% to 19% on average. Most importantly, more instances can be solved when using OBBT.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-57803</identifier>
    <identifier type="doi">10.1007/s10898-016-0450-4</identifier>
    <enrichment key="SourceTitle">Journal of Global Optimization</enrichment>
    <author>Ambros Gleixner</author>
    <submitter>Benjamin Müller</submitter>
    <author>Timo Berthold</author>
    <author>Benjamin Müller</author>
    <author>Stefan Weltge</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-16</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MINLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimization-based bound tightening</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimality-based bound tightening</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OBBT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>propagation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bound tightening</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="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/5780/ZR-15-16.pdf</file>
  </doc>
  <doc>
    <id>6629</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-12-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 5.0</title>
    <abstract language="eng">This article describes new features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite. In its central component, the constraint integer programming solver SCIP, remarkable performance improvements have been achieved for solving mixed-integer linear and nonlinear programs. On MIPs, SCIP 5.0 is about 41 % faster than SCIP 4.0 and over twice as fast on instances that take at least 100 seconds to solve. For MINLP, SCIP 5.0 is about 17 % faster overall and 23 % faster on instances that take at least 100 seconds to solve. This boost is due to algorithmic advances in several parts of the solver such as cutting plane generation and management, a new adaptive coordination of large neighborhood search heuristics, symmetry handling, and strengthened McCormick relaxations for bilinear terms in MINLPs. Besides discussing the theoretical background and the implementational aspects of these developments, the report describes recent additions for the other software packages connected to SCIP, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solver SCIP-SDP, and the parallelization framework UG.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-66297</identifier>
    <author>Ambros Gleixner</author>
    <submitter>Gregor Hendel</submitter>
    <author>Leon Eifler</author>
    <author>Tristan Gally</author>
    <author>Gerald Gamrath</author>
    <author>Patrick Gemander</author>
    <author>Robert Lion Gottwald</author>
    <author>Gregor Hendel</author>
    <author>Christopher Hojny</author>
    <author>Thorsten Koch</author>
    <author>Matthias Miltenberger</author>
    <author>Benjamin Müller</author>
    <author>Marc Pfetsch</author>
    <author>Christian Puchert</author>
    <author>Daniel Rehfeldt</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Jan Merlin Viernickel</author>
    <author>Stefan Vigerske</author>
    <author>Dieter Weninger</author>
    <author>Jonas T. Witt</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-61</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>constraint integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer nonlinear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimization solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-cut</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>branch-and-price</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>column generation framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallelization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer semidefinite programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <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="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6629/scipopt-50.pdf</file>
  </doc>
  <doc>
    <id>7517</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>2019-11-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming</title>
    <abstract language="eng">The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global epsilon-optimality with spatial branch and bound is a tight, computationally tractable relaxation. Due to both theoretical and practical considerations, relaxations of MINLPs are usually required to be convex. Nonetheless, current optimization solver can often successfully handle a moderate presence of nonconvexities, which opens the door for the use of potentially tighter nonconvex relaxations. In this work, we exploit this fact and make use of a nonconvex relaxation obtained via aggregation of constraints: a surrogate relaxation. These relaxations were actively studied for linear integer programs in the 70s and 80s, but they have been scarcely considered since. We revisit these relaxations in an MINLP setting and show the computational benefits and challenges they can have. Additionally, we study a generalization of such relaxation that allows for multiple aggregations simultaneously and present the first algorithm that is capable of computing the best set of aggregations. We propose a multitude of computational enhancements for improving its practical performance and evaluate the algorithm’s ability to generate strong dual bounds through extensive computational experiments.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-75179</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Benjamin Müller</author>
    <submitter>Benjamin Müller</submitter>
    <author>Gonzalo Muñoz</author>
    <author>Maxime Gasse</author>
    <author>Ambros Gleixner</author>
    <author>Andrea Lodi</author>
    <author>Felipe Serrano</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-55</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>surrogate relaxation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MINLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nonconvex optimization</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="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="EnBA-M">EnBA-M</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7517/ZR-19-55.pdf</file>
  </doc>
  <doc>
    <id>6450</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>2017-07-14</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On the performance of NLP solvers within global MINLP solvers</title>
    <abstract language="eng">Solving mixed-integer nonlinear programs (MINLPs) to global optimality efficiently requires fast solvers for continuous sub-problems. These appear in, e.g., primal heuristics, convex relaxations, and bound tightening methods. Two of the best performing algorithms for these sub-problems are Sequential Quadratic Programming (SQP) and Interior Point Methods. In this paper we study the impact of different SQP and Interior Point implementations on important MINLP solver components that solve a sequence of similar NLPs. We use the constraint integer programming framework SCIP for our computational studies.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-64504</identifier>
    <identifier type="doi">10.1007/978-3-319-89920-6_84</identifier>
    <enrichment key="SourceTitle">Operations Research Proceedings 2017</enrichment>
    <author>Benjamin Müller</author>
    <submitter>Benjamin Müller</submitter>
    <author>Renke Kuhlmann</author>
    <author>Stefan Vigerske</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-35</number>
    </series>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="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/6450/ZR-17-35.pdf</file>
  </doc>
  <doc>
    <id>6203</id>
    <completedYear>2018</completedYear>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-12-14</completedDate>
    <publishedDate>2017-02-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exact Methods for Recursive Circle Packing</title>
    <abstract language="eng">Packing rings into a minimum number of rectangles is an optimization problem which appears naturally in the logistics operations of the tube industry. It encompasses two major difficulties, namely the positioning of rings in rectangles and the recursive packing of rings into other rings. This problem is known as the Recursive Circle Packing Problem (RCPP). We present the first dedicated method for solving RCPP that provides strong dual bounds based on an exact Dantzig–Wolfe reformulation of a nonconvex mixed-integer nonlinear programming formulation. The key idea of this reformulation is to break symmetry on each recursion level by enumerating one-level packings, i.e., packings of circles into other circles, and by dynamically generating packings of circles into rectangles. We use column generation techniques to design a “price-and-verify” algorithm that solves this reformulation to global optimality. Extensive computational experiments on a large test set show that our method not only computes tight dual bounds, but often produces primal solutions better than those computed by heuristics from the literature.</abstract>
    <identifier type="urn">urn:nbn:de:0297-zib-62039</identifier>
    <identifier type="doi">10.1007/s10479-018-3115-5</identifier>
    <enrichment key="SourceTitle">Annals of Operations Research, S.I.: Decomposition Methods for Hard Optimization Problems, December 2018, Published as "Price-and-verify: a new algorithm for recursive circle packing using Dantzig--Wolfe decomposition"</enrichment>
    <author>Ambros Gleixner</author>
    <submitter>Benjamin Müller</submitter>
    <author>Stephen J. Maher</author>
    <author>Benjamin Müller</author>
    <author>João Pedro Pedroso</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-07</number>
    </series>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</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/6203/ringpacking.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>
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    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
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    <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>6217</id>
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    <language>eng</language>
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    <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>
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    <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/>
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    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
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    <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>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MINLP solver</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear programming</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LP solver</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simplex method</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modeling</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parallel branch-and-bound</value>
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    <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>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Steiner tree solver</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-criteria optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mixed-integer semidefinite programming</value>
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    <collection role="msc" number="65Y05">Parallel computation</collection>
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    <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>
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    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/5767/scipopt-32.pdf</file>
  </doc>
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