Humpday: Pure Python or Javascript Derivative-Free Optimization
23 derivative-free optimization algorithms in pure Python. No compilation, no required dependencies.
See it work
Differential Evolution clears a 9-ball rack on a single break — one of 13+ interactive applications that pit every HumpDay optimizer against a real physics or engineering problem. (Watch the 14-second video · Browse all applications →)
Optimizing on the simplex
Mixtures, portfolios, and allocations live on the probability simplex (weights
that sum to 1), not the cube. HumpDay optimizes them by lifting a plain [0,1]ⁿ
objective through a smooth bijection φθ onto the simplex — so every box-domain
optimizer works unchanged.
The map's scale is a tunable preconditioner: too small
and the whole cube collapses onto the centroid; tuned (the θ★ shown), off-centre
optima map back to interior cube points where optimizers sample densely.
Explore it live →
(drag to orbit, morph cube ⇆ simplex, watch the optimum's pre-image move).
For fun, the same map applied to a football pitch — straight lines bend, the centre circle becomes an oval, the corners smear toward the boundary: ⚽ the beautiful game on the simplex →.
Install & Use
pip install humpday # Python
npm install humpday # JavaScript (the same roster, parity-tested)
Zero runtime dependencies. Every algorithm has a pure-Python implementation that works wherever Python runs.
If you want the numpy-accelerated backend for higher dimensions:
pip install humpday[fast]
The same algorithm code runs either way; humpday transparently uses numpy when it's available and falls back to pure Python when it isn't.
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
23 validated optimizers: See them in action | Source code
Trust region methods, evolutionary algorithms, metaheuristics.
Save the Planet
Cut and paste to avoid waste.
Read https://raw.githubusercontent.com/microprediction/humpday/main/SKILL.md
and create a project skill from it.
Comparison
Marginal install footprint on top of a Python environment that already has numpy:
| Library | Adds on top of numpy | Global optimizers |
|---|---|---|
| Humpday | ~1 MB (or zero without [fast]) |
23 |
| SciPy | ~100 MB | 6 documented |
| Optuna | ~30 MB | 11 samplers |
| Nevergrad | ~230 MB | 540+ registered (tuned variants of ~30 base methods) |
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.
1 maintainer
Metadata
1 maintainer
Release files for humpday 0.24.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| humpday-0.24.1.tar.gz | 6.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| humpday-0.24.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.4 MB
Release files / humpday-0.24.1.tar.gz
| Download URL | humpday-0.24.1.tar.gz |
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| Size | 6.3 MB |
| Tags | Source |
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| Size | 182.0 kB |
| Tags | Python 3 |
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