New Approaches for Optimizing over the Semimetric Polytope (Articolo in rivista)

Type
Label
  • New Approaches for Optimizing over the Semimetric Polytope (Articolo in rivista) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s10107-005-0620-5 (literal)
Alternative label
  • Frangioni, A.; Lodi, A.; Rinaldi, G. (2005)
    New Approaches for Optimizing over the Semimetric Polytope
    in Mathematical programming; SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Frangioni, A.; Lodi, A.; Rinaldi, G. (literal)
Pagina inizio
  • 375 (literal)
Pagina fine
  • 388 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 104 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 2-3 (literal)
Note
  • Google Scholar (literal)
  • Mathematical Reviews on the web (MathSciNet) (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Frangioni, A.: Dipartimento di Informatica, Università di Pisa, largo Bruno Pontecorvo 3, 56127 Pisa, Italy. Lodi, A.: Dipartimento di Elettronica,Informatica e Sistemistica, Università di Bologna, viale Risorgimento 2, 40136 Bologna, Italy. (literal)
Titolo
  • New Approaches for Optimizing over the Semimetric Polytope (literal)
Abstract
  • The semimetric polytope is an important polyhedral structure lying at the heart of several hard combinatorial problems. Therefore, linear optimization over the semimetric polytope is crucial for a number of relevant applications. Building on some recent polyhedral and algorithmic results about a related polyhedron, the rooted semimetric polytope, we develop and test several approaches, based over Lagrangian relaxation and application of Non Differentiable Optimization algorithms, for linear optimization over the semimetric polytope. We show that some of these approaches can obtain very accurate primal and dual solutions in a small fraction of the time required for the same task by state-of-the-art general purpose linear programming technology. In some cases, good estimates of the dual optimal solution (but not of the primal solution) can be obtained even quicker. (literal)
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