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Python library for stochastic numerical optimization

Project description

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stochopy provides functions for sampling or optimizing objective functions with or without constraints. Its API is directly inspired by scipy’s own optimization submodule which should make the switch from one module to another straightforward.


Optimization of 2D multimodal function Styblinski-Tang using PSO.


Sampling algorithms:

  • Hamiltonian (Hybrid) Monte-Carlo (HMC),

  • Markov-Chain Monte-Carlo (McMC).

Stochastic optimizers:

  • Competitive Particle Swarm Optimization (CPSO),

  • Covariance Matrix Adaptation - Evolution Strategy (CMA-ES),

  • Differential Evolution (DE),

  • Neighborhood Algorithm (NA),

  • Particle Swarm Optimization (PSO),

  • VD-CMA.

Parallel backends:


The recommended way to install stochopy and all its dependencies is through the Python Package Index:

pip install stochopy --user

Otherwise, clone and extract the package, then run from the package location:

pip install . --user

To test the integrity of the installed package, check out this repository and run:



Refer to the online documentation for detailed description of the API and examples.

Alternatively, the documentation can be built using Sphinx:

pip install -r doc/requirements.txt
sphinx-build -b html doc/source doc/build


Given an optimization problem defined by an objective function and a feasible space:

import numpy as np

def rosenbrock(x):
   x = np.asarray(x)
   sum1 = ((x[1:] - x[:-1] ** 2) ** 2).sum()
   sum2 = np.square(1.0 - x[:-1]).sum()
   return 100.0 * sum1 + sum2

bounds = [[-5.12, 5.12], [-5.12, 5.12]]  # The number of variables to optimize is len(bounds)

The optimal solution can be found following:

from stochopy.optimize import minimize

x = minimize(rosenbrock, bounds, method="cmaes", options={"maxiter": 100, "popsize": 10, "seed": 0})

minimize returns a dictionary that contains the results of the optimization:

    fun: 3.862267657514075e-09
message: 'best solution value is lower than ftol'
   nfev: 490
    nit: 49
 status: 1
success: True
      x: array([0.99997096, 0.99993643])


Please refer to the Contributing Guidelines to see how you can help. This project is released with a Code of Conduct which you agree to abide by when contributing.

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