Parallel Optimistic Optimization

POO (Parallel Optimistic Optimization)

This is a black-box function optimization toolkit that finds the global optimum of a function given a finite budget of noisy evaluations. The algorithm does not require the knowledge of the function’s smoothness. It works for a larger class of functions than what was previously considered, especially for functions that are difficult to optimize, in a precise sense.

Reference: Jean-Bastien Grill, Michal Valko, RĂ©mi Munos: Black-box optimization of noisy functions with unknown smoothness, in Neural Information Processing Systems (NIPS 2015)

Software: POO v1.0 (Python)

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