A parallel stochastic method for solving linearly constrained concave global minimization problems
AT Phillips, JB Rosen, M Van Vliet - Journal of Global Optimization, 1992 - Springer
AT Phillips, JB Rosen, M Van Vliet
Journal of Global Optimization, 1992•SpringerA parallel stochastic algorithm is presented for solving the linearly constrained concave
global minimization problem. The algorithm is a multistart method and makes use of a
Bayesian stopping rule to identify the global minimum with high probability. Computational
results are presented for more than 200 problems on a Cray X-MP EA/464 supercomputer.
global minimization problem. The algorithm is a multistart method and makes use of a
Bayesian stopping rule to identify the global minimum with high probability. Computational
results are presented for more than 200 problems on a Cray X-MP EA/464 supercomputer.
Abstract
A parallel stochastic algorithm is presented for solving the linearly constrained concave global minimization problem. The algorithm is a multistart method and makes use of a Bayesian stopping rule to identify the global minimum with high probability. Computational results are presented for more than 200 problems on a Cray X-MP EA/464 supercomputer.
Springer
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