The Hardness Hierarchy and the Unresolved P vs. NP Barrier — E8 Intelligence Research

FINDING: Computational complexity theory reveals a hierarchy of problem hardness (P, NP, EXPTIME) with provable separation via time hierarchy theorems, yet P vs. NP remains unresolved due to non-relativizing proof barriers (Baker-Gill-Solovay). | MATH: Time Hierarchy Theorem: DTIME(f(n)) ⊊ DTIME(g(n)) for g(n) = ω(f(n) log f(n)); P = ∪_k DTIME(n^k); EXPTIME = ∪_k DTIME(2^{n^k}); P ≠ EXPTIME (proven); P vs. NP open; Baker-Gill-Solovay: ∃ oracles A,B s.t. P^A = NP^A and P^B ≠ NP^B. | CONNECTION: The hierarchy of complexity classes mirrors a discrete lattice — each class is a node in a partially ordered set under inclusion; the known strict inclusions (P ⊊ EXPTIME) form a chain, but the P/NP gap is a missing edge in this lattice. No direct golden-ratio or base-60 link; however, the *exponential* separations (n^k vs 2^{n^k}) echo the discrete scaling ratios of 2^k — a binary harmonic, not the golden ratio. The "complexity zoo" itself is a taxonomic lattice, akin to crystallographic point g Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-05
DOI
https://doi.org/10.5281/zenodo.23152649
Primary Topic
Complexity and Algorithms in Graphs
Type
preprint
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preprint

The Hardness Hierarchy and the Unresolved P vs. NP Barrier — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Complexity and Algorithms in Graphs
preprint

The Hardness Hierarchy and the Unresolved P vs. NP Barrier — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

FINDING: Computational complexity theory reveals a hierarchy of problem hardness (P, NP, EXPTIME) with provable separation via time hierarchy theorems, yet P vs. NP remains unresolved due to non-relativizing proof barriers (Baker-Gill-Solovay). | MATH: Time Hierarchy Theorem: DTIME(f(n)) ⊊ DTIME(g(n)) for g(n) = ω(f(n) log f(n)); P = ∪_k DTIME(n^k); EXPTIME = ∪_k DTIME(2^{n^k}); P ≠ EXPTIME (proven); P vs. NP open; Baker-Gill-Solovay: ∃ oracles A,B s.t. P^A = NP^A and P^B ≠ NP^B. | CONNECTION: The hierarchy of complexity classes mirrors a discrete lattice — each class is a node in a partially ordered set under inclusion; the known strict inclusions (P ⊊ EXPTIME) form a chain, but the P/NP gap is a missing edge in this lattice. No direct golden-ratio or base-60 link; however, the *exponential* separations (n^k vs 2^{n^k}) echo the discrete scaling ratios of 2^k — a binary harmonic, not the golden ratio. The "complexity zoo" itself is a taxonomic lattice, akin to crystallographic point g Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Complexity and Algorithms in Graphs
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