Abstract
In many problems of large size encountered in applications, the constraints are linear, while the objective function is a sum of two parts: a linear part involving most of the variables of the problem, and a concave part involving only a relatively small number of variables. More precisely, these problems have the form
where f: ℝn → ℝ is a concave function, ft is a polyhedron, d and y are vectors in ℝh, and n is generally much smaller than h.
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© 1996 Springer-Verlag Berlin Heidelberg
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Horst, R., Tuy, H. (1996). Decomposition of Large Scale Problems. In: Global Optimization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03199-5_8
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DOI: https://doi.org/10.1007/978-3-662-03199-5_8
Publisher Name: Springer, Berlin, Heidelberg
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