Comparative evaluation of the stochastic simplex bisection algorithm and the SciPy.Optimize module

The stochastic simplex bisection (SSB) algorithm is evaluated against the collection of optimizers in the Python SciPy.Optimize module on a prominent test set. The SSB algorithm greatly outperforms all SciPy optimizers, 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 SciPy optimizers, it sustains a high success rate. The SciPy optimizers would benefit from a more informed metaheuristic strategy and the SSB algorithm would profit from quicker local convergence and better multi-dimensional capabilities. Conversely, the local convergence of the SciPy optimizers is impressive and the multimodal capabilities of the SSB algorithm in separable dimensions are uncanny.