humpday 0.9.0

Taking the pain out of choosing a Python global optimizer

Humpday: Pure Python or Javascript Derivative-Free Optimization

Documentation & Live Demos

22 derivative-free optimization algorithms in pure Python. No compilation, just numpy.

Install & Use

pip install humpday

from humpday import minimize

def objective(x):
    return (x[0] - 2)**2 + (x[1] - 3)**2

result = minimize(objective, bounds=[(-5, 5), (-5, 5)], method='DifferentialEvolution')
print(f"Solution: {result.x}")  # [2.0, 3.0]

Algorithms

22 validated optimizers: See them in action | Source code

Trust region methods, evolutionary algorithms, metaheuristics.

Comparison

Library	Dependencies	Install Size	Global Optimizers	Pure Python
Humpday	numpy only	~1MB	22 validated	✅
SciPy	C/Fortran libs	~50MB	3	❌
Optuna	Many	~100MB	100+	❌
Nevergrad	Many	~200MB	200+	❌

Humpday's niche: When you need optimization that works anywhere Python runs, without dependencies or compilation.

License

MIT - Use freely in commercial and research projects.