Implementation of Polynomial NP-Complete Algorithms Based on the NP Verifier Simulation Framework
Authors/Creators
Description
While prior work established a verifier-based polynomial-time framework for NP, explicit deterministic machines for concrete NP-complete problems have remained elusive.
In this paper, we construct fully specified deterministic Turing Machines (DTMs) for SAT and Subset-Sum within an improved NP verifier simulation framework.
A key contribution of this work is the development of a functional implementation that bridges the gap between theoretical proofs and executable software.
Our improved feasible-graph construction yields a genuine reduction in the asymptotic polynomial degree, while optimized edge-extension mechanisms significantly improve practical execution speed.
We show that these machines generate valid witnesses, extending the framework to deterministic FNP computation without increasing complexity.
The complete Python implementation behaves in accordance with the predicted polynomial-time bounds, and the source code along with sample instances are available in a public online repository.
Notes
This paper is a follow-up to the work available at Zenodo. The reference implementation is provided via GitHub . While the repository and manuscript may undergo minor editorial polishing and corrections post-submission, these updates will not affect the core theoretical results or the established polynomial complexity bounds. This work has also been submitted to arXiv.
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P=NPImpl.pdf
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