Global solution of optimization problems with signomial parts

Authors:
Ray PöRn;Kaj-Mikael BjöRk;Tapio Westerlund
Affiliations:
Sector for Technology and Communication at Swedish Polytechnic, Wollfskavägen 33, PB 6, FIN-65201 Vasa, Finland;IAMSR at bo Akademi University, Lemminkäisenk. 14, FIN-20540 Turku, Finland;Process Design Laboratory at bo Akademi University, Biskopsgatan 8, FIN-20500 bo, Finland
Venue:
Discrete Optimization
Year:
2008

Citing 3
Cited 4

Integer and combinatorial optimization

Integer and combinatorial optimization
Studies of the behavior of recursion for the pooling problem

ACM SIGMAP Bulletin
Approximating separable nonlinear functions via mixed zero-one programs

Operations Research Letters

Convex underestimation strategies for signomial functions

Optimization Methods & Software - GLOBAL OPTIMIZATION
Using nonlinear mixed integer optimization in printed circuit board assembly

AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
An Efficient Global Approach for Posynomial Geometric Programming Problems

INFORMS Journal on Computing
A reformulation framework for global optimization

Journal of Global Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a new approach for the global solution of nonconvex MINLP (Mixed Integer NonLinear Programming) problems that contain signomial (generalized geometric) expressions is proposed and illustrated. By applying different variable transformation techniques and a discretization scheme a lower bounding convex MINLP problem can be derived. The convexified MINLP problem can be solved with standard methods. The key element in this approach is that all transformations are applied termwise. In this way all convex parts of the problem are left unaffected by the transformations. The method is illustrated by four example problems.