Diagonalization of Polynomial-Time Deterministic Turing Machines via Nondeterministic Turing Machines
Tianrong Lin

TL;DR
This paper uses a diagonalization approach with nondeterministic Turing machines to show a separation between P and NP, providing new insights into the limitations of diagonalization in relativized complexity classes.
Contribution
It introduces a novel diagonalization method against polynomial-time deterministic Turing machines using nondeterministic machines, proving P ≠ NP and exploring limitations of diagonalization in relativized settings.
Findings
Existence of a language in NP not accepted by any polynomial-time deterministic Turing machine.
Existence of a language in P accepted by machines running within any polynomial time bound.
Diagonalization via nondeterministic machines cannot be applied to relativized P vs NP problems under certain assumptions.
Abstract
The {\em diagonalization technique} was invented by Georg Cantor to show that there are more real numbers than algebraic numbers and is very crucial in {\em theoretical computer science}. In this work, we enumerate all of the polynomial-time deterministic Turing machines and diagonalize against all of them by a universal nondeterministic Turing machine. As a result, we obtain that there is a language not accepted by any polynomial-time deterministic Turing machines but accepted by a nondeterministic Turing machine running within time for any . Based on these, we further show that . That is, in this work, we present a proof that and differ. Meanwhile, we show that there exists a language in , but the machine accepting it also runs within time for all . Lastly,…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Quantum Computing Algorithms and Architecture · Complexity and Algorithms in Graphs
