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  <doc>
    <id>4635</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>205</pageFirst>
    <pageLast>219</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>LNCS 6244</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Supporting Global Numerical Optimization of Rational Functions by Generic Symbolic Convexity Tests</title>
    <parentTitle language="eng">Computer Algebra in Scientific Computing</parentTitle>
    <identifier type="isbn">978-3-642-15273-3</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-11644</enrichment>
    <author>Winfried Neun</author>
    <submitter> Engel</submitter>
    <author>Thomas Sturm</author>
    <author>Stefan Vigerske</author>
    <collection role="institutes" number="sis">Digital Data and Information for Society, Science, and Culture</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
  </doc>
  <doc>
    <id>7273</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving the performance of DICOPT in convex MINLP problems using a feasibility pump</title>
    <abstract language="eng">The solver DICOPT is based on an outer-approximation algorithm used for solving mixed- integer nonlinear programming (MINLP) problems. This algorithm is very effective for solving some types of convex MINLPs. However, there are certain problems that are diffcult to solve with this algorithm. One of these problems is when the nonlinear constraints are so restrictive that the nonlinear subproblems produced by the algorithm are infeasible. This problem is addressed in this paper with a feasibility pump algorithm, which modifies the objective function in order to efficiently find feasible solutions. It has been implemented as a preprocessing algorithm for DICOPT. Computational comparisons with previous versions of DICOPT and other MINLP solvers on a set of convex MINLPs demonstrate the effectiveness of the proposed algorithm in terms of solution quality and solving time.</abstract>
    <parentTitle language="eng">Optimization Methods and Software</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">revision under review</enrichment>
    <author>David E. Bernal</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Stefan Vigerske</author>
    <author>Francisco Trespalacios</author>
    <author>Ignacio E. Grossmann</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>9057</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst>47</pageFirst>
    <pageLast>78</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>61</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="jpn">制約整数計画ソルバ SCIP の並列化</title>
    <abstract language="jpn">制約整数計画（CIP: Constraint Integer Programs）は，制約プログラミング（CP: Constraint Programming），混合整数計画（MIP: Mixed Integer Programming），充足可能性問題（SAT: Satisfability Problem）の研究分野におけるモデリング技術と解法を統合している．その結果，制約整数計画は，広いクラスの最適化問題を扱うことができる．SCIP（Solving Constraint Integer Programs）は，CIP を解くソルバとして実装され，Zuse Institute Berlin（ZIB）の研究者を中心として継続的に拡張が続けられている．本論文では，著者らによって開発された SCIP に対する2 種類の並列化拡張を紹介する．一つは，複数計算ノード間で大規模に並列動作する ParaSCIPである．もう一つは，複数コアと共有メモリを持つ 1 台の計算機上で（スレッド）並列で動作する FiberSCIP である．ParaSCIP は，HLRN II スーパーコンピュータ上で，一つのインスタンスを解くために最大 7,168 コアを利用した動作実績がある．また，統計数理研究所の Fujitsu PRIMERGY RX200S5 上でも，最大 512 コアを利用した動作実績がある．統計数理研究所のFujitsu PRIMERGY RX200S5 上では，これまでに最適解が得られていなかった MIPLIB2010のインスタンスである dg012142 に最適解を与えた．</abstract>
    <abstract language="eng">The paradigm of constraint integer programming (CIP) combines modeling and solving techniques from the fields of constraint programming (CP), mixed-integer programming (MIP) and　satisfability problem (SAT). This paradigm allows us to address a wide　range of optimization problems. SCIP is an implementation of the idea of CIP and is now being continuously extended by a group of researchers centered at Zuse Institute Berlin (ZIB). This paper introduces two parallel extensions of SCIP. One is ParaSCIP, which is intended to run on a large scale distributed memory computing environment, and the other is FiberSCIP, intended to run on a shared memory computing environment. ParaSCIP has been run successfully on the HLRN II supercomputer utilizing up to 7,168 cores to solve a single difficult MIP. It has also been tested on an ISM supercomputer (Fujitsu PRIMERGY RX200S5 using up to 512 cores). The previously unsolved instance dg012142 from MIPLIB2010 was solved by using the ISM supercomputer.</abstract>
    <parentTitle language="jpn">統計数理</parentTitle>
    <identifier type="url">https://www.ism.ac.jp/editsec/toukei/pdf/61-1-047.pdf</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">in press</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Yuji Shinano</author>
    <submitter>Yuji Shinano</submitter>
    <author>Tobias Achterberg</author>
    <author>Timo Berthold</author>
    <author>Stefan Heinz</author>
    <author>Thorsten Koch</author>
    <author>Stefan Vigerske</author>
    <author>Michael Winkler</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6755</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>237</pageFirst>
    <pageLast>265</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-09-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">QPLIB: A Library of Quadratic Programming Instances</title>
    <abstract language="eng">This paper describes a new instance library for Quadratic Programming (QP), i.e., the family of continuous and (mixed)-integer optimization problems where the objective function, the constrains, or both are quadratic. QP is a very diverse class of problems, comprising sub-classes of problems ranging from trivial to undecidable. This diversity is reflected in the variety of solution methods for QP, ranging from entirely combinatorial ones to completely continuous ones, including many for which both aspects are fundamental. Selecting a set of instances of QP that is at the same time not overwhelmingly onerous but sufficiently challenging for the many different interested communities is therefore important. We propose a simple taxonomy for QP instances that leads to a systematic problem selection mechanism. We then briefly survey the field of QP, giving an overview of theory, methods and solvers. Finally, we describe how the library was put together, and detail its final contents.</abstract>
    <parentTitle language="eng">Mathematical Programming Computation</parentTitle>
    <identifier type="doi">10.1007/s12532-018-0147-4</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Fabio Furini</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Emiliano Traversi</author>
    <author>Pietro Belotti</author>
    <author>Antonio Frangioni</author>
    <author>Ambros Gleixner</author>
    <author>Nick Gould</author>
    <author>Leo Liberti</author>
    <author>Andrea Lodi</author>
    <author>Ruth Misener</author>
    <author>Hans Mittelmann</author>
    <author>Nikolaos V. Sahinidis</author>
    <author>Stefan Vigerske</author>
    <author>Angelika Wiegele</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>9143</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>703</pageFirst>
    <pageLast>731</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>15</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-08-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Computational Study of Perspective Cuts</title>
    <abstract language="eng">The benefits of cutting planes based on the perspective function are well known for many specific classes of mixed-integer nonlinear programs with on/off structures. However, we are not aware of any empirical studies that evaluate their applicability and computational impact over large, heterogeneous test sets in general-purpose solvers. This paper provides a detailed computational study of perspective cuts within a linear programming based branch-and-cut solver for general mixed-integer nonlinear programs. Within this study, we extend the applicability of perspective cuts from convex to nonconvex nonlinearities. This generalization is achieved by applying a perspective strengthening to valid linear inequalities which separate solutions of linear relaxations. The resulting method can be applied to any constraint where all variables appearing in nonlinear terms are semi-continuous and depend on at least one common indicator variable. Our computational experiments show that adding perspective cuts for convex constraints yields a consistent improvement of performance, and adding perspective cuts for nonconvex constraints reduces branch-and-bound tree sizes and strengthens the root node relaxation, but has no significant impact on the overall mean time.</abstract>
    <parentTitle language="eng">Mathematical Programming Computation</parentTitle>
    <identifier type="doi">10.1007/s12532-023-00246-4</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2023-05-04</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-81821</enrichment>
    <author>Ksenia Bestuzheva</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Ambros Gleixner</author>
    <author>Stefan Vigerske</author>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
  </doc>
  <doc>
    <id>4718</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions</title>
    <abstract language="eng">In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints.&#13;
&#13;
We describe a two-stage approach to solve the resulting complex and numerically difficult feasibility problem. The first phase consists of four distinct algorithms applying linear, and methods for complementarity constraints to compute possible settings for the discrete decisions. The second phase employs a precise continuous programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances that are significantly larger than networks that have appeared in the mathematical programming literature before.</abstract>
    <parentTitle language="eng">Optimization Methods and Software</parentTitle>
    <identifier type="doi">10.1080/10556788.2014.888426</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-16531</enrichment>
    <submitter>Robert Schwarz</submitter>
    <author>Marc Pfetsch</author>
    <author>Armin Fügenschuh</author>
    <author>Björn Geißler</author>
    <author>Nina Geißler</author>
    <author>Ralf Gollmer</author>
    <author>Benjamin Hiller</author>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Alexander Martin</author>
    <author>Antonio Morsi</author>
    <author>Jessica Rövekamp</author>
    <author>Lars Schewe</author>
    <author>Martin Schmidt</author>
    <author>Rüdiger Schultz</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Claudia Stangl</author>
    <author>Marc Steinbach</author>
    <author>Stefan Vigerske</author>
    <author>Bernhard Willert</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="persons" number="schweiger">Schweiger, Jonas</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>4891</id>
    <completedYear>2012</completedYear>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>695</pageFirst>
    <pageLast>711</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>2</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards globally optimal operation of water supply networks</title>
    <abstract language="eng">This paper is concerned with optimal operation of pressurized water supply networks at a fixed point in time. We use a mixed-integer nonlinear programming (MINLP) model incorporating both the nonlinear physical laws and the discrete decisions such as switching pumps on and off. We demonstrate that for instances from our industry partner, these stationary models can be solved to ε-global optimality within small running times using problem-specific presolving and state-of-the-art MINLP algorithms. In our modeling, we emphasize the importance of distinguishing between what we call real and imaginary flow, i.e., taking into account that the law of Darcy-Weisbach correlates pressure difference and flow along a pipe if and only if water is available at the high pressure end of a pipe. Our modeling solution extends to the dynamic operative planning problem.</abstract>
    <parentTitle language="eng">Numerical Algebra, Control and Optimization</parentTitle>
    <identifier type="doi">10.3934/naco.2012.2.695</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-15603</enrichment>
    <author>Ambros Gleixner</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Harald Held</author>
    <author>Wei Huang</author>
    <author>Stefan Vigerske</author>
    <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="MATHEON-B20">MATHEON-B20</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>4818</id>
    <completedYear>2012</completedYear>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted>2012</thesisYearAccepted>
    <language>eng</language>
    <pageFirst>739</pageFirst>
    <pageLast>748</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>2</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analyzing the computational impact of MIQCP solver components</title>
    <abstract language="eng">We provide a computational study of the performance of a state-of-the-art solver for nonconvex mixed-integer quadratically constrained programs (MIQCPs). Since successful general-purpose solvers for large problem classes necessarily comprise a variety of algorithmic techniques, we focus especially on the impact of the individual solver components. The solver SCIP used for the experiments implements a branch-and-cut algorithm based on a linear relaxation to solve MIQCPs to global optimality. Our analysis is based on a set of 86 publicly available test instances.</abstract>
    <parentTitle language="eng">Numerical Algebra, Control and Optimization</parentTitle>
    <identifier type="doi">10.3934/naco.2012.2.739</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-17754</enrichment>
    <submitter>Ambros Gleixner</submitter>
    <author>Timo Berthold</author>
    <author>Ambros Gleixner</author>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="berthold">Berthold, Timo</collection>
    <collection role="persons" number="heinz">Heinz, Stefan</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MATHEON-B20">MATHEON-B20</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6739</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>11</pageFirst>
    <pageLast>30</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>30</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-11-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">FiberSCIP - A shared memory parallelization of SCIP</title>
    <abstract language="eng">Recently, parallel computing environments have become significantly popular. In order to obtain the benefit of using parallel computing environments, we have to deploy our programs for these effectively. This paper focuses on a parallelization of SCIP (Solving Constraint Integer Programs), which is a mixed-integer linear programming solver and constraint integer programming framework available in source code. There is a parallel extension of SCIP named ParaSCIP, which parallelizes SCIP on massively parallel distributed memory computing environments. This paper describes FiberSCIP, which is yet another parallel extension of SCIP to utilize multi-threaded parallel computation on shared memory computing environments, and has the following contributions: First, we present the basic concept of having two parallel extensions, and the relationship between them and the parallelization framework provided by UG (Ubiquity Generator), including an implementation of deterministic parallelization. Second, we discuss the difficulties in achieving a good performance that utilizes all resources on an actual computing environment, and the difficulties of performance evaluation of the parallel solvers. Third, we present a way to evaluate the performance of new algorithms and parameter settings of the parallel extensions. Finally, we demonstrate the current performance of FiberSCIP for solving mixed-integer linear programs (MIPs) and mixed-integer nonlinear programs (MINLPs) in parallel.</abstract>
    <parentTitle language="eng">INFORMS Journal on Computing</parentTitle>
    <identifier type="doi">10.1287/ijoc.2017.0762</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-42595</enrichment>
    <author>Yuji Shinano</author>
    <submitter>Yuji Shinano</submitter>
    <author>Stefan Heinz</author>
    <author>Stefan Vigerske</author>
    <author>Michael Winkler</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>6408</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>563</pageFirst>
    <pageLast>593</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>33</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-06-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">SCIP: Global Optimization of Mixed-Integer Nonlinear Programs in a Branch-and-Cut Framework</title>
    <abstract language="eng">This paper describes the extensions that were added to the constraint integer programming framework SCIP in order to enable it to solve convex and nonconvex mixed-integer nonlinear programs (MINLPs) to global optimality. SCIP implements a spatial branch-and-bound algorithm based on a linear outer-approximation, which is computed by convex over- and underestimation of nonconvex functions. An expression graph representation of nonlinear constraints allows for bound tightening, structure analysis, and reformulation. Primal heuristics are employed throughout the solving process to find feasible solutions early. We provide insights into the performance impact of individual MINLP solver components via a detailed computational study over a large and heterogeneous test set.</abstract>
    <parentTitle language="eng">Optimization Methods &amp; Software</parentTitle>
    <identifier type="doi">10.1080/10556788.2017.1335312</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-59377</enrichment>
    <author>Stefan Vigerske</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Ambros Gleixner</author>
    <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="MATHEON-B20">MATHEON-B20</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="Siemens">Siemens</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
