Abstract
AbstractThe sequential simplex optimization algorithm has been translated into non‐Euclidean space. A study of the algorithm in this space suggests that the path of convergence of the simplex depends on the properties of the space so that different convergence paths can be obtained even with the same starting points. The path of convergence approaches the path observed in Euclidean space as the metric constant of the hyperbolic space increases.
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