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Efficient Methods for Large-Scale Unconstrained Optimization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F06%3A00341250" target="_blank" >RIV/67985807:_____/06:00341250 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Efficient Methods for Large-Scale Unconstrained Optimization

  • Original language description

    This contribution contains a description of efficient methods for large scale unconstrained optimization. Many of them have been developed recently by the authors. It concerns limited memory methods for general smooth optimization, variable-metric bundlemethods for partially separable nonsmooth optimization, hybrid methods for sparse least squares and methods for solving large-scale trust-region subsproblem.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BA - General mathematics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/IAA1030405" target="_blank" >IAA1030405: Development of software system for solving large-scale problems of nonlinear and nonsmooth optimization</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2006

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Large-Scale Nonlinear Optimization

  • ISBN

    978-0-387-30063-4

  • ISSN

  • e-ISSN

  • Number of pages

    26

  • Pages from-to

  • Publisher name

    Springer

  • Place of publication

    New York

  • Event location

    Erice

  • Event date

    Jun 22, 2004

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000235840800012