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A global optimization method for nonconvex separable programming problems
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DOI：10.1016/S0377-2217(98)00243-4.png)
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Abstract
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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. (C) 1999 Elsevier Science B.V. All rights reserved.
Keywords:
goal programming
piecewise linear function
separable programming
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