Resolution of P ̸= NP: Compression Barriers, Non-Relativizing Arguments, and Entropy Formalization

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Main Author: Fisher, Christopher
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Published: Zenodo 2025
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author Fisher, Christopher
author_facet Fisher, Christopher
contents <h3>A Formal Resolution of the P vs NP Problem via Entropy Barriers, Compression Limits, and Non-Relativizing Structural Complexity</h3> <p>This paper presents a mathematically rigorous proof that the complexity classes P and NP are not equal. The approach integrates entropy theory, instance compression, and circuit complexity to demonstrate that no deterministic polynomial-time algorithm can decide all NP-complete problems.</p> <p>The proof begins by establishing an entropy barrier, showing that any attempt to distinguish among exponentially many valid solutions with polynomially bounded resources results in an information-theoretic contradiction. A compression impossibility theorem is introduced to show that instances of NP-complete problems cannot be reduced to polynomial length while preserving solvability, violating known bounds on Kolmogorov complexity.</p> <p>Additionally, the paper demonstrates that solving NP-complete problems requires superpolynomial circuit size, and that no uniform or nonuniform polynomial-sized family of algorithms can bypass this requirement. The entire argument is non-relativizing, meaning it remains valid regardless of oracle access or relativized computational models.</p> <p>A section is also dedicated to the limits of quantum computing, clarifying that quantum algorithms cannot collapse NP into P due to fundamental constraints on state representation and computational entropy.</p> <p>The result is a complete and modular proof that P does not equal NP, resolving one of the most significant open questions in theoretical computer science.</p>
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spellingShingle Resolution of P ̸= NP: Compression Barriers, Non-Relativizing Arguments, and Entropy Formalization
Fisher, Christopher
P ≠ NP proof
theoretical computer science
entropy barrier
compression impossibility
non-relativizing arguments
circuit complexity
Kolmogorov complexity
NP-complete problems
decision tree depth
cryptography implications
Clay Millennium Problem
computational complexity
algorithmic barriers
structural complexity
quantum complexity
<h3>A Formal Resolution of the P vs NP Problem via Entropy Barriers, Compression Limits, and Non-Relativizing Structural Complexity</h3> <p>This paper presents a mathematically rigorous proof that the complexity classes P and NP are not equal. The approach integrates entropy theory, instance compression, and circuit complexity to demonstrate that no deterministic polynomial-time algorithm can decide all NP-complete problems.</p> <p>The proof begins by establishing an entropy barrier, showing that any attempt to distinguish among exponentially many valid solutions with polynomially bounded resources results in an information-theoretic contradiction. A compression impossibility theorem is introduced to show that instances of NP-complete problems cannot be reduced to polynomial length while preserving solvability, violating known bounds on Kolmogorov complexity.</p> <p>Additionally, the paper demonstrates that solving NP-complete problems requires superpolynomial circuit size, and that no uniform or nonuniform polynomial-sized family of algorithms can bypass this requirement. The entire argument is non-relativizing, meaning it remains valid regardless of oracle access or relativized computational models.</p> <p>A section is also dedicated to the limits of quantum computing, clarifying that quantum algorithms cannot collapse NP into P due to fundamental constraints on state representation and computational entropy.</p> <p>The result is a complete and modular proof that P does not equal NP, resolving one of the most significant open questions in theoretical computer science.</p>
title Resolution of P ̸= NP: Compression Barriers, Non-Relativizing Arguments, and Entropy Formalization
topic P ≠ NP proof
theoretical computer science
entropy barrier
compression impossibility
non-relativizing arguments
circuit complexity
Kolmogorov complexity
NP-complete problems
decision tree depth
cryptography implications
Clay Millennium Problem
computational complexity
algorithmic barriers
structural complexity
quantum complexity
url https://doi.org/10.5281/zenodo.15087349