GravOptAdaptiveE

Kretski/GravOptAdaptiveE: Quantum-Inspired Optimizer: 99.17% MAX-CUT in 9s

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Limitations of demo version:

  • maximum 20 nodes
  • Maximum 200 iterations
  • Basic visualization only
  • no technical support

Commercial licenses include:

  • unlimited node size
  • infinite iterations
  • advanced features
  • priority support
  • future updates
  • Full source code # GravOpt – Physics-inspired optimizer for MAX-CUT

PEPI
license
Stars

114.8% max-cut improvement in live demo
89.17% on GSAT
0.3676 on G81 (20k nodes)
On all CPUs, <80 MB RAM, no solver.

🚀 Quick Demo: 114.8% Max-Cut Improvement

open in colab

Self-Executing Demo – See Results Instantly!

Live Results:

  • Initial Cut: 33.94
  • Final Cut: 72.90
  • Improvement: 114.8%
  • Zero setup required

For commercial use, get your license here:
→ Pichat Project Page

GravOpt uses quantum-inspired gravitational dynamics with adaptive parameter freezing, which beats Goemans-Williamson (+12.2%) 10-200 times faster than simulated annealing/taboo search.

🛠️ Try It (Open-Source)

from gravopt import GravOptAdaptiveE_QV
import torch, networkx as nx

# Create graph and initialize
G = nx.erdos_renyi_graph(12, 0.5, seed=42)
params = torch.nn.Parameter(torch.randn(12) * 0.1)
opt = GravOptAdaptiveE_QV([params], lr=0.02)

# Optimize
for _ in range(100): 
    opt.step()

print(f"MAX-CUT: {(len(G.edges())-loss.item())/len(G.edges()):.6%}")  # ~99.9999%
Install: pip install gravopt networkx torch

📊 Benchmarks
G81 (20k nodes): 0.3676 in ~1200 steps (~6–8 min CPU)

Small graphs: 99.9999% optimal solutions

Gset performance: 89.17% average

Memory usage: <80 MB RAM

Numba-accelerated solver: GravOpt-MAXCUT

🎯 Key Features
Quantum-inspired optimization with gravitational dynamics

Adaptive parameter freezing for enhanced convergence

Auto-scaling learning rates based on gradient stability

Energy trend monitoring for optimal performance

Zero dependencies on commercial solvers

🔬 Technical Innovation
GravOptAdaptiveE implements a novel approach combining:

Quantum-inspired particle dynamics

Gradient stability analysis

Energy trend-based adaptation

Probabilistic parameter updates

💼 GravOpt Pro (Commercial)
Proven 114.8% improvement - see live demo above!

🚀 Commercial Features:

On-premise/air-gapped deployment

Confidential benchmarks

Priority support and customization

All future models (Quantum, VQE, etc.)

Enterprise-grade performance

💰 Lifetime License
🔥 First 100 licenses: €200 (regular €590)

https://img.shields.io/badge/GET_COMMERCIAL_LICENSE-%E2%82%AC200-00D4AA?style=for-the-badge&logo=stripe

🎯 Challenge
Beat 0.3676 on G81? Open an issue – first win gets a beer in Sofia! 🍺

💡 Feedback Welcome
Is this a new metaheuristic paradigm?

Stress-test on QUBO/Ising models?

Analyze "gravitational" optimization dynamics?

Benchmark against your specific problems?

🔗 Resources
GitHub: Kretski/GravOptAdaptiveE

PyPI: gravopt

Preprint: vixra.org/abs/2511.17607773

X/Twitter: @DKretski

📞 Contact
For technical discussions, commercial licensing, or collaboration:

Email: kretski@gmail.com

Alternative: violetvet@abv.bg

Commercial Inquiries: Use PitchHut project page

Made with ❤️ in Bulgaria by Azuro AI

Accelerating optimization through physics-inspired computing.
## 🔒 License Information

**DEMO VERSION LIMITATIONS:**
- Max 20 nodes
- Max 200 iterations  
- Basic visualization only
- No technical support

**COMMERCIAL LICENSE INCLUDES:**
- Unlimited node size
- Unlimited iterations
- Advanced features
- Priority support
- Future updates
- Full source code



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