Computational comparison of two methods for constrained global optimization

AT Phillips, JB Rosen - Journal of Global Optimization, 1994 - Springer
AT Phillips, JB Rosen
Journal of Global Optimization, 1994Springer
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 …
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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