The stochastic simplex bisection (SSB) algorithm is evaluated against the collection of optimizers in the python *** module on a prominent test set. The SSB algorithm greatly outperforms all scipyoptimizers, save one...
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(纸本)9788360810668
The stochastic simplex bisection (SSB) algorithm is evaluated against the collection of optimizers in the python *** module on a prominent test set. The SSB algorithm greatly outperforms all scipyoptimizers, save one, in exactly half the cases. It does slightly worse on quadratic functions, but excels at trigonometric ones, highlighting its multimodal prowess. Unlike the scipyoptimizers, it sustains a high success rate. The scipyoptimizers would benefit from a more informed metaheuristic strategy and the SSB algorithm would profit from quicker local convergence and better multidimensional capabilities. Conversely, the local convergence of the scipyoptimizers is impressive and the multimodal capabilities of the SSB algorithm in separable dimensions are uncanny.
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