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Global optimization for generalized geometric programs with mixed free-sign variables. (English) Zbl 1226.90078
Summary: Many optimization problems are formulated as generalized geometric programming (GGP) containing signomial terms $$f(\mathbf X)\cdot g(\mathbf Y)$$, where $$\mathbf X$$ and $$\mathbf Y$$ are continuous and discrete free-sign vectors, respectively. By effectively convexifying $$f(\mathbf X)$$ and linearizing $$g(\mathbf Y)$$, this study globally solves a GGP with a lower number of binary variables than are used in current GGP methods. Numerical experiments demonstrate the computational efficiency of the proposed method.

##### MSC:
 90C26 Nonconvex programming, global optimization
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