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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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