A new global optimization approach for convex multiplicative programming |
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Authors: | Yuelin Gao |
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Institution: | a School of Information and Computation Science, North University for Nationalities, Yinchuan 750021, China b School of Natural and Applied Science, Northwestern Polytechnical University, Xi’an 710068, China c School of Economics and Management, Tongji University, Shanghai 200092, China |
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Abstract: | In this paper, by solving the relaxed quasiconcave programming problem in outcome space, a new global optimization algorithm for convex multiplicative programming is presented. Two kinds of techniques are employed to establish the algorithm. The first one is outer approximation technique which is applied to shrink relaxation area of quasiconcave programming problem and to compute appropriate feasible points and to raise the capacity of bounding. And the other one is branch and bound technique which is used to guarantee global optimization. Some numerical results are presented to demonstrate the effectiveness and feasibility of the proposed algorithm. |
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Keywords: | Global optimization Convex multiplicative programming Outer approximation Branch-and-bound Outcome space |
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