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

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author Fisher, Christopher
author_facet Fisher, Christopher
contents <p>This paper presents a complete and rigorous proof that P is not equal to NP, resolving one of the most fundamental open problems in theoretical computer science. The proof integrates three independent approaches: an entropy-based decision tree complexity barrier, a compression impossibility theorem grounded in Kolmogorov complexity, and a non-relativizing circuit complexity separation. Together, these demonstrate that NP-complete problems cannot be solved in polynomial time by any deterministic algorithm.</p> <p>The entropy argument shows that distinguishing among all possible solutions requires an exponential number of steps. The compression theorem proves that no polynomial-time function can reduce an NP-complete instance to a smaller equivalent form while preserving decidability. The circuit complexity result establishes that solving NP-complete problems requires circuits of exponential size, beyond the scope of polynomial-time computation.</p> <p>These results hold universally, even under oracle and quantum models, making the separation between P and NP robust across computational paradigms. The proof draws from foundational principles in complexity theory, including Shannon entropy, Kolmogorov complexity, and structural bounds from circuit lower bounds literature.</p> <p>This work has significant implications for cryptography, optimization, computational hardness, and our broader understanding of algorithmic limits.</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
<p>This paper presents a complete and rigorous proof that P is not equal to NP, resolving one of the most fundamental open problems in theoretical computer science. The proof integrates three independent approaches: an entropy-based decision tree complexity barrier, a compression impossibility theorem grounded in Kolmogorov complexity, and a non-relativizing circuit complexity separation. Together, these demonstrate that NP-complete problems cannot be solved in polynomial time by any deterministic algorithm.</p> <p>The entropy argument shows that distinguishing among all possible solutions requires an exponential number of steps. The compression theorem proves that no polynomial-time function can reduce an NP-complete instance to a smaller equivalent form while preserving decidability. The circuit complexity result establishes that solving NP-complete problems requires circuits of exponential size, beyond the scope of polynomial-time computation.</p> <p>These results hold universally, even under oracle and quantum models, making the separation between P and NP robust across computational paradigms. The proof draws from foundational principles in complexity theory, including Shannon entropy, Kolmogorov complexity, and structural bounds from circuit lower bounds literature.</p> <p>This work has significant implications for cryptography, optimization, computational hardness, and our broader understanding of algorithmic limits.</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.15088639