Abstrakti
The alpha-reformulation (alpha R) technique can be used to transform any nonconvex twice-differentiable mixed-integer nonlinear programming problem to a convex relaxed form. By adding a quadratic function to the nonconvex function it is possible to convexify it, and by subtracting a piecewise linearization of the added function a convex underestimator will be obtained. This reformulation technique is implemented in the a global optimization (alpha GO) algorithm solving the specified problem type to global optimality as a sequence of reformulated subproblems where the piecewise linear functions are refined in each step. The tightness of the underestimator has a large impact on the efficiency of the solution process, and in this paper it is shown how it is possible to reduce the approximation error by utilizing a piecewise quadratic spline function defined on smaller subintervals. The improved underestimator is also applied to test problems illustrating its performance.
Alkuperäiskieli | Ei tiedossa |
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Otsikko | 11th International Conference on Chemical and Process Engineering - selected papers of ICheaP11 |
Toimittajat | Sauro Pierucci, Jiří J. Klemeš |
Kustantaja | Associazione Italiana di Ingegneria Chimica |
Sivut | 1321–1326 |
Sivumäärä | 6 |
ISBN (painettu) | 978-88-95608-23-5 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 2013 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | International Conference on Chemical and Process Engineering (ICheaP) - 11th International Conference on Chemical and Process Engineering (ICheaP) Kesto: 2 kesäk. 2013 → 5 kesäk. 2013 |
Konferenssi
Konferenssi | International Conference on Chemical and Process Engineering (ICheaP) |
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Ajanjakso | 02/06/13 → 05/06/13 |