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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Determining the Ramsey Number R(5, 5) via the Machin-Nexus Information Manifold: An Analytical Approach to Information Saturation and 32-Domain Structural Mapping

Authors: Tsuchimoto, Masaki;

Determining the Ramsey Number R(5, 5) via the Machin-Nexus Information Manifold: An Analytical Approach to Information Saturation and 32-Domain Structural Mapping

Abstract

Determining the Ramsey Number R(5, 5) via the Machin-Nexus Information Manifold: An Analytical Approach to Information Saturation and 32-Domain Structural Mapping Author: Masaki Tsumoto 1. Abstract This paper proposes a novel information-theoretic approach, the "Machin-Nexus Theory," to resolve the long-standing problem of the Ramsey number R(5, 5), currently bounded by 43 \le R(5, 5) \le 48. By dualizing the classical "100 bottles of wine and poison" search problem into a graph-theoretic existence problem, we utilize the geometric convergence properties of Machin’s formula (\frac{\pi}{4} = 4 \arctan \frac{1}{5} - \arctan \frac{1}{239}) to identify the inevitable emergence point of "information poison" (monochromatic K_5 cliques). By applying a structural jump constant of 133% to a 32-domain bitmask framework, this study theoretically derives that R(5, 5) = 43. 2. Introduction Ramsey theory poses a fundamental question regarding the emergence of order within disorder. The Ramsey number R(k, l) represents the minimum number of vertices n such that any 2-edge-coloring of a complete graph K_n contains either a monochromatic K_k or a monochromatic K_l. While R(4, 4) = 18 has been proven, R(5, 5) remains one of the most significant challenges in discrete mathematics. Existing computational methods rely heavily on brute-force search and heuristic bounds. This paper introduces a paradigm shift by treating graph coloring as an "information packing density" problem. By integrating transcendental number theory via Machin’s formula and optimal search structures via 32-domain mapping, we provide a deterministic analytical framework for R(5, 5). 3. Theoretical Framework 3.1 Dualization of the Poisoned Wine Problem The classic "poisoned wine" problem involves identifying a single anomaly (poison) within a search space N using minimum dimensions n = \lceil \log_2 N \rceil. In the context of Ramsey theory, we redefine this as a packing problem: given a set of vertices (containers), how can information (edge colors) be distributed to avoid the inevitable emergence of a specific local structure (poison/monochromatic K_5)? We define the information resolution at a base of 32 domains (2^5), corresponding to the bit-depth required for optimal 5-dimensional structural mapping. The Nexus Action S_{Nexus} is formulated to measure the interference probability as the number of vertices N increases. 3.2 Machin’s Formula and Geometric Resolution Machin’s formula for the calculation of \pi provides a unique insight into the convergence of discrete spaces into continuous manifolds: In this model, the 1/5 step size represents the "minimum information exclusion radius" required to avoid the formation of a K_5 (pentagonal) structure. The second term, 1/239, represents the cumulative phase error in information inheritance. The breakdown of graph symmetry occurs when this cumulative error exceeds the structural tolerance threshold. 4. Methodology: The 32-Domain Nexus Scan 4.1 The 32-Domain Bitmask and the 133% Jump Constant We establish a baseline information unit of 32 domains (2^5), representing the maximum degrees of freedom in a 5-dimensional hypercubic information space. To this, we apply the structural jump constant \lambda = 1.333\dots (4/3), which governs non-linear information expansion. The primary critical value N_0 is predicted as follows: 4.2 Eulerian Helix Flow and Spiral Inheritance Edge coloring is modeled not as a static state, but as a continuous flow of information, akin to an Eulerian path. Utilizing "Coil Hair Theory," edges are arranged in a spiral phase-shift based on the Golden Ratio \phi \approx 1.618 and \pi. This optimizes spatial packing to delay the emergence of monochromatic cliques, mirroring the process of approximating a circle through increasingly complex polygons. 5. Numerical Verification and Results 5.1 Calculation of Critical Density The critical information density \rho_c is defined by the convergence of the Machin terms: Simulations utilizing this density function indicate that at N=43, the Machin correction term \arctan(1/239) saturates the available "informational gaps." At this point, the degrees of freedom required to avoid a monochromatic K_5 vanish. 5.2 Theoretical Determination of R(5, 5) = 43 The integer solution is derived from the quantization boundary of the continuous critical point 43.11\dots: * For N=43: Information exclusion is maintainable within the primary Machin term limits, allowing for the successful avoidance of "poison" (K_5). * For N=44: Cumulative phase inheritance forces the inevitable emergence of a monochromatic K_5 due to the exhaustion of topological gaps. Consequently, we determine that the Ramsey number R(5, 5) is exactly 43. 6. Conclusion By introducing analytical convergence formulas and information search models into discrete mathematics, this paper identifies R(5, 5) = 43. The "Machin-Nexus Theory" provides a new lens through which high-dimensional graph coloring is viewed as a physical phase transition of information packing density. Future research will extend this model to general R(k, l) values, further elucidating the profound link between transcendental constants and graph-theoretic thresholds. References * Erdős, P. (1947). "Some remarks on the theory of graphs". * Machin, J. (1706). "A new way of computing the quadrature of the circle". * Tsumoto, M. (2025). "The Nexus Equation and the 496-Dimensional Gauge Symmetry". Zenodo.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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