Welcome to noisyopt’s documentation!¶
noisyopt is concerned with solving an (possibly bound-constrained) optimization problem of the kind
where evaluations of the function f are not directly possible, but only evaluation of the function F. The expectation value of F is f, but F also depends on some random noise. Such optimization problems are known under various names, such as stochastic approximation, stochastic optimization/programming, or noisy optimization. They arise in various contexts from Simulation-based optimization, to machine learning.
To solve such optimization problems the package currently implements two (derivative-free) algorithms:
- a robust pattern search algorithm with an adaptive number of function evaluations [Mayer2016]
- a stochastic approximation algorithm, namely simultaneous perturbation stochastic approximation [Spall1998]
Further algorithms might be added in the future – you are invited to contribute! The package also contains a function to find the root of a noisy function by a bisection algorithm with an adaptive number of function evaluations.
Noisyopt is concerned with local optimization, if you are interested in global optimization you might want to have a look at Bayesian optimization techniques (see e.g. scikit-optimize). For optimizing functions that are not noisy take a look at scipy.optimize.
You can install noisyopt using pip:
pip install noisyopt
Minimal usage example:
import numpy as np from noisyopt import minimizeCompass def obj(x): return (x**2).sum() + 0.1*np.random.randn() res = minimizeCompass(obj, x0=[1.0, 2.0], deltatol=0.1, paired=False)
For further documentation see below or the usage examples.
If you use and like this package, you might want to read and cite the papers describing the implemented algorithms:
|[Mayer2016]||Mayer, A.; Mora, T.; Rivoire, O. & Walczak, A. M. Diversity of immune strategies explained by adaptation to pathogen statistics. PNAS, 2016, 113(31), 8630-8635. Relevant section is in the Supplementary Information entitled “Pattern-search based optimization for problems with noisy function evaluations”.|
|[Spall1998]||Spall, JC. Implementation of the simultaneous perturbation algorithm for stochastic optimization. Aerospace and Electronic Systems, IEEE Transactions on, IEEE, 1998, 34, 817-823|