Matthew F. McLaughlin

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A parallel stochastic algorithm is implemented for solving the linearly constrained concave global optimization problem. The algorithm uses a multistart technique which repeatedly employs two phases, the global phase and the local phase. The global phase creates a random search direction to find a vertex of the linearly constrained feasible region. The(More)
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