powell-opt 0.1.0
Python bindings for the Powell method from scirs2-optimize
License: MIT
Requires Python: >=3.9
Keywords: optimization, powell, scientific, minimize

# powell-opt

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Python bindings for the _Powell's method_ optimisation algorithm from the `scirs2-optimize` Rust library.

## Installation

```bash
pip install powell-opt
```

## About Powell's Method

Powell's method is a derivative-free optimization algorithm that minimizes functions by performing sequential one-dimensional searches along different directions. It's particularly useful for functions that:

- Cannot be easily differentiated
- Have discontinuities or non-smooth regions
- Have relatively few dimensions

## Usage

```python
import powell_opt as po

def rosenbrock(x):
    """Rosenbrock function - a classical test case for optimization"""
    return 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2

# Initial guess
x0 = [0.0, 0.0]

# Set options (optional)
options = po.Options(maxiter=1000, ftol=1e-6)

# Minimize using Powell's method
result = po.minimize(rosenbrock, x0, options)

print(f"Solution: {result.x}")
print(f"Function value: {result.fun}")
print(f"Number of iterations: {result.nit}")
print(f"Success: {result.success}")
```
⇣
```
Solution: [1.0000000000000002, 1.0000000000000007]
Function value: 4.979684464207637e-30
Number of iterations: 19
Success: True
```

- Find this at `examples/basic_usage.py` and in the tests as `test_powell_rosenbrock()`

## API Reference

### `minimize(func, x0, options=None)`

Minimizes a scalar function using Powell's method.

**Parameters:**
- `func`: A callable that takes a list of parameters and returns a scalar value
- `x0`: Initial guess (list of parameters)
- `options`: Optional `Options` object with algorithm parameters

**Returns:**
- `MinimizeResult` object containing the optimization results

### `Options`

Class for configuring the Powell optimization algorithm.

**Parameters:**
- `maxiter`: Maximum number of iterations (optional)
- `ftol`: Relative tolerance for convergence in function value (optional)
- `gtol`: Relative tolerance for convergence in gradient norm (optional)
- `disp`: Whether to print convergence progress messages (default: False)
- `eps`: Small value used for numerical stability in calculations (optional)
- `finite_diff_rel_step`: Relative step size for finite difference approximation of derivatives (optional)
- `return_all`: Whether to return intermediate solutions from all iterations (default: False)

### `MinimizeResult`

Class containing optimization results.

**Attributes:**
- `x`: Solution array
- `fun`: Value of the objective function at the solution
- `nfev`: Number of function evaluations
- `nit`: Number of iterations
- `message`: Description of the termination reason
- `success`: Whether the optimizer exited successfully

## Performance

This implementation leverages Rust's performance through PyO3 bindings, making it significantly faster than pure Python implementations for computationally intensive problems.

(TODO: benchmark!)

## Contributing

Maintained by [lmmx](https://github.com/lmmx). Contributions welcome!

1. **Issues & Discussions**: Please open a GitHub issue or discussion for bugs, feature requests, or questions.
2. **Pull Requests**: PRs are welcome!
   - Install the dev extra (e.g. with [uv](https://docs.astral.sh/uv/): `uv pip install -e .[dev]`)
   - Run tests (when available) and include updates to docs or examples if relevant.
   - If reporting a bug, please include the version and the error message/traceback if available.

## License

MIT License

Copyright (c) 2025 Louis Maddox