2003
DOI: 10.1016/s0098-1354(02)00254-5
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Some convexifications in global optimization of problems containing signomial terms

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Cited by 55 publications
(23 citation statements)
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“…Hence, future work will focus on the development of solution methods that exploit this special structure. One alternative includes the use of convexification techniques similar to the ones presented by Westerlund et al 68,69 in search for global solutions. Our surrogate-based MINLP model includes the surrogate models of the permanent units, the reformulation of the surrogate models we just introduced for conditional units, and the algebraic equivalents of the logic constraints in (13).…”
Section: Superstructure Modeling Approachmentioning
confidence: 99%
“…Hence, future work will focus on the development of solution methods that exploit this special structure. One alternative includes the use of convexification techniques similar to the ones presented by Westerlund et al 68,69 in search for global solutions. Our surrogate-based MINLP model includes the surrogate models of the permanent units, the reformulation of the surrogate models we just introduced for conditional units, and the algebraic equivalents of the logic constraints in (13).…”
Section: Superstructure Modeling Approachmentioning
confidence: 99%
“…, k}. The fact that BilinApprox is an approximation follows because by (15) we have that for k → ∞, if x = q ∈ [x L , x U ] then w → qy. Although different approximations of the term xy are possible, the one presented in this section employs a reasonably small number of variables and is not likely to restrict the feasible region of the problem.…”
Section: Approximation Of Bilinear Productsmentioning
confidence: 98%
“…A signomial function is a sum of signomial terms. In [15], a set of transformations of the form x k = f k (z k ) are proposed, where x k is a problem variable, z k is a variable in the reformulated problem and f k is suitable function that can be either exponential or power. This yields an opt-reformulation where all the inequality constraints are convex, and the variables z and the associated (inverse) defining constraints x k = f k (z k ) are added to the reformulation for all k ∈ K (over each signomial term of each signomial constraint).…”
Section: Signomial Programming Based Relaxationmentioning
confidence: 99%
“…al [11] and Han-Li et al [12] (see Appendix C). By following this technique a convex relaxation for r C = x a A x b B can be obtained as follows: is an upper bound for r C .…”
Section: Step 1: Finding a Convex Gdp Relaxationmentioning
confidence: 99%