cocoa

COCOA (COllection of Continuous Optimization Algorithms) is a large suite of efficient algorithms written in C++ for the optimization of continuous black-box functions (mostly without using derivative information).

Main advantages:

• provides a single unified interface for all algorithms
• provides a variety of classical algorithms with state-of-the-art improvements (e.g. automatic parameter adaptation)
• convenient wrappers for Python with a user-friendly API

Installation

To use this library in a Python project, you will need:

• C++ compiler (e.g., MS Build Tools)
• git
• pybind11

Then install directly from source:

pip install git+https://github.com/mike-gimelfarb/cocoa

Usage

Simple example to optimize the 10D Rosenbrock function.

import numpy as np
from cocoaopt import ActiveCMAES


# function to optimize
def fx(x):
    total = 0.0
    for x1, x2 in zip(x[:-1], x[1:]):
        total += 100 * (x2 - x1 ** 2) ** 2 + (1 - x1) ** 2
    return total


n = 10  # dimension of problem
alg = ActiveCMAES(mfev=10000, tol=1e-4, np=20)
sol = alg.optimize(fx,
                   lower=-10 * np.ones(n),
                   upper=10 * np.ones(n),
                   guess=np.random.uniform(low=-10., high=10., size=n))
print(sol)

This will print the following output:

x*: 0.999989 0.999999 1.000001 1.000007 1.000020 1.000029 1.000102 1.000183 1.000357 1.000689 
objective calls: 6980
constraint calls: 0
B/B constraint calls: 0
converged: yes