A global optimization method for nonconvex separable programming problems |
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Institution: | 1. Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand;2. University Politehnica of Bucharest, 313 Spl. Independenţei, Bucharest 060042, Romania |
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Abstract: | Conventional methods of solving nonconvex separable programming (NSP) problems by mixed integer programming methods requires adding numerous 0–1 variables. In this work, we present a new method of deriving the global optimum of a NSP program using less number of 0–1 variables. A separable function is initially expressed by a piecewise linear function with summation of absolute terms. Linearizing these absolute terms allows us to convert a NSP problem into a linearly mixed 0–1 program solvable for reaching a solution which is extremely close to the global optimum. |
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