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May 16, 20260 citationsOpen Access

Multi-Chain Multi-Tentacle Optimisation for NP-Hard Search

Multi-Chain Multi-Tentacle Optimisation: Synthesising MCCO and Spacetime Digital Exhaust for Collapse-Centred NP-Hard Search

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Authors

HRH Y Rao

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Overview

Theoretical framework synthesises MCCO and SDE for optimising NP-Hard problems, suggesting new complexities.

Key Points

  • This paper aims to theoretically unify the MCCO and SDE frameworks to enhance NP-Hard search optimization.
  • Synthesised N chains of CPTP evolution with shared peripheral factors for optimization.
  • Defined MCMT architecture with unique tentacle weighting functions.
  • Proven three structural propositions including CPTP preservation and information-theoretic gain.
  • Theoretical performance analysis yields complexity bounds for multiple NP-Hard problem classes.
  • Information-theoretic orderings demonstrate advantages over independent SDE methods.
  • Establishes links to other frameworks and indicates cross-box applicability.
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Cite This Study

H Y Rao (2026) studied this question.

synapsesocial.com/papers/6a080a71a487c87a6a40c62chttps://doi.org/10.5281/zenodo.20178725
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