Publikation:

LMBOPT : a limited memory method for bound-constrained optimization

Lade...
Vorschaubild

Dateien

Kimiaei_2-1amcqdcshpdw28.pdf
Kimiaei_2-1amcqdcshpdw28.pdfGröße: 1.96 MBDownloads: 110

Datum

2022

Autor:innen

Kimiaei, Morteza
Neumaier, Arnold

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Link zur Lizenz

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Mathematical Programming Computation. Springer. 2022, 14(2), pp. 271-318. ISSN 1867-2949. eISSN 1867-2957. Available under: doi: 10.1007/s12532-021-00213-x

Zusammenfassung

Recently, Neumaier and Azmi gave a comprehensive convergence theory for a generic algorithm for bound constrained optimization problems with a continuously differentiable objective function. The algorithm combines an active set strategy with a gradient-free line search CLS along a piecewise linear search path defined by directions chosen to reduce zigzagging. This paper describes LMBOPT, an efficient implementation of this scheme. It employs new limited memory techniques for computing the search directions, improves CLS by adding various safeguards relevant when finite precision arithmetic is used, and adds many practical enhancements in other details. The paper compares LMBOPT and several other solvers on the unconstrained and bound constrained problems from the CUTEst collection and makes recommendations on which solver to use and when. Depending on the problem class, the problem dimension, and the precise goal, the best solvers are LMBOPT, ASACG, and LMBFG-EIG-MS.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

Schlagwörter

Bound constrained optimization, Exact gradient, Limited memory technique, Robust line search method

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690KIMIAEI, Morteza, Arnold NEUMAIER, Behzad AZMI, 2022. LMBOPT : a limited memory method for bound-constrained optimization. In: Mathematical Programming Computation. Springer. 2022, 14(2), pp. 271-318. ISSN 1867-2949. eISSN 1867-2957. Available under: doi: 10.1007/s12532-021-00213-x
BibTex
@article{Kimiaei2022-06LMBOP-56295,
  year={2022},
  doi={10.1007/s12532-021-00213-x},
  title={LMBOPT : a limited memory method for bound-constrained optimization},
  number={2},
  volume={14},
  issn={1867-2949},
  journal={Mathematical Programming Computation},
  pages={271--318},
  author={Kimiaei, Morteza and Neumaier, Arnold and Azmi, Behzad}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/56295">
    <dcterms:title>LMBOPT : a limited memory method for bound-constrained optimization</dcterms:title>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-01-21T12:21:56Z</dcterms:available>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/56295/1/Kimiaei_2-1amcqdcshpdw28.pdf"/>
    <dc:creator>Neumaier, Arnold</dc:creator>
    <dc:creator>Azmi, Behzad</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Neumaier, Arnold</dc:contributor>
    <dc:contributor>Azmi, Behzad</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-01-21T12:21:56Z</dc:date>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Kimiaei, Morteza</dc:creator>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/56295/1/Kimiaei_2-1amcqdcshpdw28.pdf"/>
    <dc:language>eng</dc:language>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dcterms:abstract xml:lang="eng">Recently, Neumaier and Azmi gave a comprehensive convergence theory for a generic algorithm for bound constrained optimization problems with a continuously differentiable objective function. The algorithm combines an active set strategy with a gradient-free line search CLS along a piecewise linear search path defined by directions chosen to reduce zigzagging. This paper describes LMBOPT, an efficient implementation of this scheme. It employs new limited memory techniques for computing the search directions, improves CLS by adding various safeguards relevant when finite precision arithmetic is used, and adds many practical enhancements in other details. The paper compares LMBOPT and several other solvers on the unconstrained and bound constrained problems from the CUTEst collection and makes recommendations on which solver to use and when. Depending on the problem class, the problem dimension, and the precise goal, the best solvers are LMBOPT, ASACG, and LMBFG-EIG-MS.</dcterms:abstract>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/56295"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dc:contributor>Kimiaei, Morteza</dc:contributor>
    <dcterms:issued>2022-06</dcterms:issued>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Unbekannt
Diese Publikation teilen