A global optimization method for nonconvex separable programming problems

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 i...

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Bibliographic Details
Published in:European journal of operational research 1999-09, Vol.117 (2), p.275-292
Main Authors: Li, Han-Lin, Yu, Chian-Son
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Goal programming
Integer programming
Mathematical programming
Operational research and scientific management
Operational research. Management science
Operations research
Piecewise linear function
Separable programming
Studies
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Summary: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.
ISSN:0377-2217
1872-6860
DOI:10.1016/S0377-2217(98)00243-4