bboptpy

bboptpy is a large suite of efficient algorithms written in C++ for the optimization of black-box functions.

Main advantages:

• single unified interface for Python with a user-friendly API
• variety of classical and modern algorithms with state-of-the-art improvements that are robust and very fast
• transparent implementation that makes it easy to build upon.

Full documentation and list of supported algorithms and functions is found here.

Installation

To use this library in a Python project, you will need a C++ compiler for Python and git (e.g. conda install git). Then install directly:

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

Algorithms Supported

The following algorithms are currently fully supported with Python wrappers:

• Univariate:
  • Branch and Bound
  • Brent Methods:
    • Local Brent
    • Global Brent
  • Calvin Method
  • Davies-Swann-Campey Method
  • Fibonacci Algorithm
  • Golden Section Search
  • Piyavskii Method
• Multivariate:
  • Unconstrained:
    • Adaptive Coordinate Descent (ACD)
    • AMaLGaM IDEA
    • Basin Hopping
    • Controlled Random Search (CRS)
    • Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES):
      • Vanilla CMA-ES
      • Active CMA-ES
      • Cholesky CMA-ES
      • Limited Memory CMA-ES
      • Separable CMA-ES
      • IPOP CMA-ES
      • BIPOP CMA-ES
    • Differential Evolution (DE):
      • JADE
      • L-SHADE
      • SANSDE
      • SSDE
    • Differential Search (DSA)
    • Exponential Natural Evolution Strategy (xNES)
    • Novel Self-Adaptive Harmony Search (NSHS)
    • Hessian Evolutionary Strategy (HE-ES)
    • Self-Adaptive Multi-Population JAYA
    • Adaptive Nelder-Mead Method
    • Particle Swarm Optimization (PSO):
      • Adaptive PSO (APSO)
      • Competitive PSO (CSO)
      • Cooperative Co-Evolving PSO (CCPSO)
      • Self-Learning PSO (SLPSO)
    • Powell's Methods:
      • BOBYQA
      • NEWUOA
    • PRAXIS
    • Rosenbrock Method

Usage

Simple example to optimize a univariate function:

import numpy as np
from bboptpy import Brent

# function to optimize
def fx(x):
    return np.sin(x) + np.sin(10 * x / 3)

alg = Brent(mfev=20000, atol=1e-6)
sol = alg.optimize(fx, lower=2.7, upper=7.5, guess=np.random.uniform(2.7, 7.5))
print(sol)

This will print the following output:

x*: 5.1457349293974861
calls to f: 10
converged: 1

Simple example to optimize a multivariate function:

import numpy as np
from bboptpy 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(-10, 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

Citation

To cite this repository, either use the link in the sidebar, or the following bibtext entry:

@software{gimelfarb2024bboptpy,
author = {Gimelfarb, Michael},
license = {LGPL-2.1+},
title = {{bboptpy: Powerful and scalable black-box optimization algorithms for Python and C++.}},
url = {https://github.com/mike-gimelfarb/bboptpy},
year = {2024}
}

To cite the authors of the individual algorithms, please see the references in the comments heads of the respective C++ source files.