Convexification of different classes of non-convex MINLP problems

Research output: Contribution to journalArticle


Research units

  • Åbo Akademi University


In the present paper, convexification strategies for certain kinds of discrete and integer non-convex optimization problems are introduced and discussed. We show how to solve problems with both posynomial and negative binomial terms in the constraints. The convexification technique may in some cases be generalized to include continuous variables. Posynomial functions are non-convex and for such functions no straightforward methods for finding the optimal solution exist. Such functions appear frequently in different kinds of chemical engineering problems. The different transformation techniques are illustrated in the form of short examples. The techniques are finally applied to a large, bilinear, trim loss problem regularly encountered at paper-converting mills.


Original languageEnglish
Pages (from-to)439-448
Number of pages10
JournalComputers and Chemical Engineering
Issue number3
Publication statusPublished - 28 Feb 1999
MoE publication typeA1 Journal article-refereed

    Research areas

  • Binomials, Convexification, Discrete and integer variables, Mixed integer non-linear programming, Optimization, Posynomials

ID: 6323113