<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>8597</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning to Use Local Cuts</title>
    <abstract language="eng">We propose a machine learning approach to address a specific algorithmic question that arises during the solving process of a mixed-integer linear programming problem, namely, whether to use cutting planes only at the root node or also at internal nodes of the branch-and-bound search tree, or equivalently, whether to run a cut-and-branch or rather a branch-and-cut algorithm. Within a supervised regression framework, we develop three machine learning models, Linear Model, Random Forest and Neural Network, for predicting the relative performance between the two methods, local-cut and no-local-cut. Hence, through an extensive computational study conducted with FICO Xpress over a large test bed of problems, we evaluate the produced strategies, and we show that they are able to provide, upon the existing policies, a significant improvement to the performance of the solver. &#13;
In fact, a variant of the random forest suggested in the present work has already been implemented by the development team of Xpress, and released with version 8.13 of the software.</abstract>
    <enrichment key="opus.source">publish</enrichment>
    <advisor>Timo Berthold</advisor>
    <author>Matteo Francobaldi</author>
    <submitter>Matteo Francobaldi</submitter>
    <advisor>Ralf Borndörfer</advisor>
    <advisor>Tim Conrad</advisor>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mathematical Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <collection role="ccs" number="I.">Computing Methodologies</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <thesisGrantor>Freie Universität Berlin</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-zib/files/8597/Francobaldi_Learning_to_Use_Local_Cuts.pdf</file>
  </doc>
</export-example>
