Global Minimization of Separable Concave Functions under Linear Constraints with Totally Unimodular Matrices
Two types of new finite branch and bound algorithms are proposed for global minimization of a separable concave function under linear constraints with a totally unimodular matrix and additional box constraints. The key idea for establishing these algorithms is based upon the fact that the underlying problem can be viewed as an integer global optimization problem. For the case that a fixed number of the components of the objective function is nonlinear, an upper bound for the running time is given, which is polynomial in the data of the box constraints.
KeywordsGlobal optimization integer global optimization separable programming concave minimization branch and bound unimodularity
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