Abstract
For constrained concave global minimization problems, two very different solution techniques have been investigated. The first such method is a stochastic mulitstart approach which typically finds, with high probability, all local minima for the problem. The second method is deterministic and guarantees a global minimum solution to within any user specified tolerance. It is the purpose of this paper to make a careful comparison of these two methods on a range of test problems using separable concave objectives over compact polyhedral sets, and to investigate in this way the advantages and disadvantages of each method. A direct computational comparison, on the same set of over 140 problems, is presented.
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Phillips, A.T., Rosen, J.B. Computational comparison of two methods for constrained global optimization. J Glob Optim 5, 325–332 (1994). https://doi.org/10.1007/BF01096682
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DOI: https://doi.org/10.1007/BF01096682