Lambda_G Optimizer

High-performance route optimization using geometric energy minimization.

Performance

Installation

pip install lambda-g-optimizer

Quick Start

Command Line

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

Python API

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):

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.

Author

Abhishek Srivastava

• ORCID: 0009-0006-7495-5039
• GitHub: @0x-auth

License

Dual Licensed:

• Open Source: AGPLv3 - Free for open-source projects
• Commercial: Commercial License - For proprietary use

If you use Lambda_G in a commercial product or SaaS without open-sourcing your code, you need a commercial license.

Contact: bits.abhi@gmail.com