Refinement strategies for piecewise linear functions utilized by reformulation-based techniques for global optimization

A1 Originalartikel i en vetenskaplig tidskrift (referentgranskad)

Interna författare/redaktörer

Publikationens författare: Andreas Lundell, Tapio Westerlund
Publiceringsår: 2013
Tidskrift: Computer Aided Chemical Engineering
Tidskriftsakronym: COMPUT-AIDED CHEM EN
Volym: 32
Artikelns första sida, sidnummer: 529
Artikelns sista sida, sidnummer: 534
Antal sidor: 6
ISSN: 1570-7946


The signomial global optimization algorithm is a method for solving nonconvex mixed-integer signomial problems to global optimality. A convex underestimation is produced by replacing nonconvex signomial terms with convex underestimators obtained through single-variable power and exponential transformations in combination with linearization techniques. The piecewise linear functions used in the linearizations are iteratively refined by adding breakpoints until the termination criteria are met. Depending on the strategy used for adding the breakpoints, the complexity of the reformulated problems as well as the solution time of these vary. One possibility is to initially add several breakpoints thus obtaining a tight convex underestimation in the first iteration at the cost of a more complex reformulated problem. This breakpoint strategy is compared to the normal strategies of iteratively adding more breakpoints through illustrative examples and test problems.


convex underestimators, global optimization, MINLP, piecewise linear functions, reformulation techniques, SGO algorithm, signomial functions

Senast uppdaterad 2019-09-12 vid 03:37