A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Method for solving generalized convex nonsmooth mixed-integer nonlinear programming problems
Tekijät: Eronen VP, Kronqvist J, Westerlund T, Mäkelä MM, Karmitsa N
Kustantaja: SPRINGER
Kustannuspaikka: 000410819100007
Julkaisuvuosi: 2017
Journal: Journal of Global Optimization
Tietokannassa oleva lehden nimi: JOURNAL OF GLOBAL OPTIMIZATION
Lehden akronyymi: J GLOBAL OPTIM
Vuosikerta: 69
Numero: 2
Aloitussivu: 443
Lopetussivu: 459
Sivujen määrä: 17
ISSN: 0925-5001
eISSN: 1573-2916
DOI: https://doi.org/10.1007/s10898-017-0528-7
Verkko-osoite: 10.1007/s10898-017-0528-7
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/26899604
In this paper, we generalize the extended supporting hyperplane algorithm for a convex continuously differentiable mixed-integer nonlinear programming problem to solve a wider class of nonsmooth problems. The generalization is made by using the subgradients of the Clarke subdifferential instead of gradients. Consequently, all the functions in the problems are assumed to be locally Lipschitz continuous. The algorithm is shown to converge to a global minimum of an MINLP problem if the objective function is convex and the constraint functions are f degrees-pseudoconvex. With some additional assumptions, the constraint functions may be f degrees-quasiconvex.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
