Convex underestimation for posynomial functions of positive variables

Han-Lin Li*, Jung Fa Tsai, Christodoulos A. Floudas


研究成果: Article同行評審

16 引文 斯高帕斯(Scopus)


The approximation of the convex envelope of nonconvex functions is an essential part in deterministic global optimization techniques (Floudas in Deterministic Global Optimization: Theory, Methods and Application, 2000). Current convex underestimation algorithms for multilinear terms, based on arithmetic intervals or recursive arithmetic intervals (Hamed in Calculation of bounds on variables and underestimating convex functions for nonconvex functions, 1991; Maranas and Floudas in J Global Optim 7:143-182, (1995); Ryoo and Sahinidis in J Global Optim 19:403-424, (2001)), introduce a large number of linear cuts. Meyer and Floudas (Trilinear monomials with positive or negative domains: Facets of convex and concave envelopes, pp. 327-352, (2003); J Global Optim 29:125-155, (2004)), introduced the complete set of explicit facets for the convex and concave envelopes of trilinear monomials with general bounds. This study proposes a novel method to underestimate posynomial functions of strictly positive variables.

頁(從 - 到)333-340
期刊Optimization Letters
出版狀態Published - 6月 2008


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