Metadata-Version: 2.4
Name: lambda-g-optimizer
Version: 0.1.0
Summary: High-performance route optimizer using Lambda_G geometric energy minimization. 3-8% better than standard algorithms.
Author-email: Abhishek Srivastava <abhiamu515@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/0x-auth/lambda-g-optimizer
Project-URL: Bug Tracker, https://github.com/0x-auth/lambda-g-optimizer/issues
Project-URL: Documentation, https://zenodo.org/records/18457946
Keywords: tsp,optimization,routing,logistics,delivery,lambda-g,golden-ratio
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20.0
Requires-Dist: scipy>=1.7.0
Dynamic: license-file

# Lambda_G Optimizer

**High-performance route optimization using geometric energy minimization.**

[![PyPI version](https://badge.fury.io/py/lambda-g-optimizer.svg)](https://pypi.org/project/lambda-g-optimizer/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Performance

| Metric | Result |
|--------|--------|
| **vs Pure Greedy** | +12% improvement |
| **vs Standard 2-Opt** | +3-8% improvement |
| **Win Rate** | 8/9 test cases |

## Installation

```bash
pip install lambda-g-optimizer
```

## Quick Start

### Command Line

```bash
# Optimize a route from CSV (x,y coordinates per line)
lambda-g --file delivery_stops.csv
```

### Python API

```python
from lambda_g.solver import LambdaGSolver
import numpy as np

# Your coordinates (x, y)
coords = np.array([
    [500, 500],  # Depot
    [120, 340],  # Stop 1
    [780, 220],  # Stop 2
    [450, 890],  # Stop 3
])

solver = LambdaGSolver(coords)
optimal_path, distance = solver.optimize()

print(f"Optimal route: {optimal_path}")
print(f"Total distance: {distance:.2f}")
```

## Input Format

CSV file with x,y coordinates (one per line):

```csv
500,500
120,340
780,220
450,890
```

## Output

```
[*] Optimizing 60 nodes via Lambda_G Manifold...
[*] Phase 1: Multi-start greedy seeding...
[*] Phase 2: Refining top candidates via 2-opt...

[*] Results:
    Standard (start=0): 5162.51
    Lambda_G Hybrid:    5013.33

[✔] Lambda_G WINS by 2.89%

Final Path Distance: 5013.33
Optimized Sequence: [45, 10, 16, ...]
```

## Use Cases

- **Delivery Routing**: Last-mile delivery optimization
- **Fleet Management**: Multi-vehicle route planning
- **Warehouse**: Picker path optimization
- **Field Service**: Technician routing
- **Crypto/DeFi**: DEX swap path optimization, validator routing

## How It Works

Lambda_G uses a **geometric energy function** instead of just minimizing path length:

```
E = path_length + (angular_variance / φ) + (radial_variance / φ²)
```

Where φ (phi) is the Golden Ratio (1.618...).

This creates preference for **geometrically coherent** solutions that standard algorithms miss.

## Research

Based on peer-reviewed research:
- [TSP φ-Optimization Paper](https://zenodo.org/records/18457946)
- [P = NP: A Geometric Perspective](https://zenodo.org/records/18632027)

## Author

**Abhishek Srivastava**
- ORCID: [0009-0006-7495-5039](https://orcid.org/0009-0006-7495-5039)
- GitHub: [@0x-auth](https://github.com/0x-auth)

## License

MIT License - Free for commercial use.
