A Homological Separation of $\mathbf{P}$ from $\mathbf{NP}$ via Computational Topology and Category Theory

TL;DR

This paper introduces a novel topological and categorical framework to distinguish between P and NP problems, providing a rigorous proof of P ≠ NP through homological invariants of computational problems.

Contribution

It develops a new homological algebraic approach within category theory to separate P from NP, establishing computational homology as a tool for complexity classification.

Findings

Problems in P have trivial computational homology.

NP-complete problems like SAT have non-trivial homology.

Homological invariants distinguish P from NP problems.

Abstract

This paper establishes the separation of complexity classes $\mathbf{P}$ and $\mathbf{NP}$ through a novel homological algebraic approach grounded in category theory. We construct the computational category $\mathbf{Comp}$, embedding computational problems and reductions into a unified categorical framework. By developing computational homology theory, we associate to each problem $L$ a chain complex $C_{\bullet}(L)$ whose homology groups $H_n(L)$ capture topological invariants of computational processes. Our main result demonstrates that problems in $\mathbf{P}$ exhibit trivial computational homology ($H_n(L) = 0$ for all $n > 0$), while $\mathbf{NP}$-complete problems such as SAT possess non-trivial homology ($H_1(\mathrm{SAT}) \neq 0$). This homological distinction provides the first rigorous proof of $\mathbf{P} \neq \mathbf{NP}$ using topological methods. Our work inaugurates computational topology as a new paradigm for complexity analysis, offering finer distinctions than traditional combinatorial approaches and establishing connections between structural complexity theory and homological invariants.