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Preprint . 2025
License: CC BY SA
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY SA
Data sources: Datacite
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A Minimal Constructive Proof That P ≠ NP via Witness Incompressibility

Une preuve constructive minimale que 𝑃 ≠ 𝑁 𝑃 P  =NP via l'incompressibilité des témoins
Authors: Murphy, Andrew J;

A Minimal Constructive Proof That P ≠ NP via Witness Incompressibility

Abstract

Une preuve uniforme, formellement vérifiée, que P ≠ NP, fondée sur l’incompressibilité des témoins. Aucun oracle. Aucune hypothèse. Aucune heuristique. Uniquement des bornes de Kolmogorov, des arguments de dénombrement, et une contradiction. Cet article présente une preuve constructive minimale que P ≠ NP dans le modèle classique de machine de Turing, sans recourir à des encodages non calculables ni à des approximations issues de la théorie de la complexité. En isolant des instances du langage NP dont les témoins sont prouvés incompressibles, nous démontrons que toute machine déterministe en temps polynomial capable de produire de tels témoins violerait nécessairement les bornes imposées par la théorie de l’information. Il en résulte une contradiction. L’approche est entièrement formelle, uniformément constructive, et résiste aux trois grandes barrières métamathématiques connues : la relativisation, les preuves naturelles et l’algebrization. Cette preuve pourrait contribuer à résoudre une question ouverte depuis cinquante ans. Elle ne repose sur aucune hypothèse de difficulté algorithmique, mais sur l’incompatibilité entre calcul déterministe et structures de témoins à forte entropie. La méthode est rigoureuse, minimale et vérifiable. Elle pourrait également servir de cadre pour d’autres séparations en théorie de la complexité, telles que L ≠ P, NP ≠ BQP, ou les frontières informationnelles dans la hiérarchie polynomiale.

A formally verified, uniform constructive resolution that P ≠ NP, derived via witness incompressibility. No oracles. No assumptions. No heuristics. Just Kolmogorov bounds, counting arguments, and a contradiction. This paper presents a minimal constructive resolution that P ≠ NP in the classical Turing model, without relying on uncomputable encodings or complexity-theoretic proxies. By isolating NP instances whose witnesses are provably incompressible, we show that any polynomial-time algorithm capable of generating such witnesses would necessarily violate information-theoretic bounds. The result is a contradiction. The approach is fully formal, uniformly constructive, and resistant to all known metamathematical barriers: relativization, natural proofs, and algebrization. This proof may close a fifty-year open question. It does not rely on assumptions about hardness, but instead on the incompatibility between deterministic computation and high-entropy witness structures. The method is rigorous, minimal, and verifiable. It may also serve as a template for future separations in complexity theory, including L ≠ P, NP ≠ BQP, and entropy-based boundaries within the polynomial hierarchy.

We provide a constructive resolution that P ≠ NP by contradiction, establishing that no deterministic polynomial-time algorithm can generate valid witnesses for all instances of canonical NP-complete problems. The argument constructs, for every sufficiently large input length n, a dense subset of problem instances whose valid witnesses are provably incompressible in the sense of Kolmogorov complexity. Assuming P = NP implies the existence of a uniform algorithm that outputs such witnesses from their corresponding inputs in polynomial time, which in turn yields a program of description length at most n + c that outputs an incompressible string of complexity exceeding n + δ. This contradiction is derived purely from classical counting bounds and does not depend on approximating or computing the Kolmogorov function. The proof is non-relativizing, non-algebrizing, and non-naturalizable. It is fully constructive, strictly uniform, and does not rely on unproven cryptographic or circuit assumptions. This establishes an unconditional separation between P and NP in the classical Turing model.

Keywords

Entropy (information), Diagonalisation, Entropy, relativisation, Information Theory, Kolmogorov complexity, L vs P, Polynomial, Mathematical model, Constructive proof, Provability, Information, Polynomial time, Deterministic computation, Diagonalization, Polynomial-time algorithms, Mathematical Computing, Structural complexity theory, P vs NP, Theory of computation, NP-complete, Natural proofs barrier, uncomputability, Computational science, Non-relativizing proof, Circuit complexity, NP-completeness, Computational complexity, Proof, computational barriers, Algebrization, Information-theoretic lower bound, Algorithms, NP vs BQP, Information theory, Uniform witness generation, Logic, Cryptographic hardness, Science, Complexity class separation, algorithms, Mathematical analysis, Polynomial-time algorithm, Logic and formal systems, Turing machine, Complexity class, Theoretical computer science, polynomial computation, Decision Theory, Fuzzy Logic, FOS: Mathematics, Witness generation, Formal Verification, Cryptographic hardness assumptions, Probability, Meta-theorem separation, computability, Computability, Entropy bounds, Data Science, Pure mathematics, Models, Theoretical, Applied mathematics, Probability Theory, Incompressibility, Mathematics/methods, Provability and uncomputability, Computation, Mathematical logic, Cryptography, kolmogorov, relativization, complexity, Oracle, Mathematics, Deterministic model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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