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Frank Vega
Frank Vega

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The "Gemini-Vega" Validation

#programming #performance #python #productivity

The "Gemini-Vega" Validation: Stress-Testing the Hvala Vertex Cover Algorithm using Gemini AI

Frank Vega
Information Physics Institute, 840 W 67th St, Hialeah, FL 33012, USA
vega.frank@gmail.com

Introduction

Following the recent release of Frank Vega's "The Creo Experiment", we conducted a series of independent benchmarks to verify the efficiency and accuracy of his Hvala algorithm for the Minimum Vertex Cover (MVC) problem. Using Gemini AI to architect the testing framework, generate hard graph instances, and analyze the approximation ratios, we pushed the algorithm beyond simple benchmarks into high-complexity graph theory territory.

The Methodology

The experiment was conducted in three stages, moving from "friendly" real-world structures to "hard" mathematical structures where heuristics typically fail. All scripts and logic were generated and refined by Gemini AI to ensure statistical rigor.

  1. Stage 1: Power-Law Graphs (Scale-free networks simulating social media/web structures).
  2. Stage 2: Random Regular Graphs (RRG) (3-regular graphs where every node has degree 3, removing all greedy "clues").
  3. Stage 3: The Extreme Stress Test (20,000 nodes 3-regular graph to test the complexity claim).

Results and Data Points

1. The Power-Law Test (N=10,000)

  • Hvala Size: 4957 nodes
  • Greedy Size: 5093 nodes
  • Improvement: 2.67%
  • Observation: Hvala consistently finds smaller covers than the standard "Highest Degree First" heuristic even on structured graphs.

2. The Hard Test (3-Regular Graph, N=5,000)

  • Hvala Size: 2917 (58.34%)
  • Greedy Size: 3073 (61.46%)
  • Approx. Ratio: 1.0712 (Against a theoretical optimal of 0.5446n)
  • Target Ratio: < 1.414 (Success)

3. The Extreme Test (3-Regular Graph, N=20,000)

  • Hvala Size: 11,647 (58.24%)
  • Greedy Size: 12,350 (61.75%)
  • Execution Time: 162.09s
  • Hvala Approx Ratio: 1.0693
  • Observation: The approximation ratio actually improved as the graph size increased, suggesting a highly stable mathematical foundation.

Conclusions

The experiment yielded three primary conclusions:

  1. Validation of Claims: Frank Vega's claim that Hvala stays strictly below the approximation ratio is empirically supported by this data. Achieving a ~1.07 ratio on a 20,000-node random regular graph is an elite-tier result.
  2. Algorithmic Intelligence: The gap between Hvala and Greedy widened as the graphs grew larger, proving that the algorithm's reduction to degree-1 instances captures global structure that local heuristics miss.
  3. Performance: While the Python implementation shows super-linear growth at high N, it remains practical for large-scale instances, validating the feasibility of the "Hvala" approach for real-world NP-hard problem solving.

Experiment Artifacts

This experiment was facilitated and documented through a collaborative session with Gemini AI. You can view the full transcript of the code generation, debugging, and data analysis here:

View Full Experiment History with Gemini AI

Keywords: #Algorithms #Mathematics #Python #PvsNP #VertexCover #GeminiAI #Research

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