Algebraic Construction of Localized Topological Degree Statistics Operator (No.4) and Rigorous Proof of Dirichlet Energy Positive Boundedness
This paper presents the formal algebraic derivation and systematic validation of the Localized Topological Degree Statistics Operator ($\mathcal{M}_{\text{degree}}$, denoted as Operator 4), a foundational component of the Phase 1 Homogeneous Metric Operator Cluster within the Status-Relational Entropy (SRE) dynamics framework. Designed to counteract the non-continuous stepping noise induced by the horizon fragmentation of distributed Actors and to eliminate zero-degree vacuum singularities at sparse boundary interfaces, this operator instantiates an analytical homogeneous metric by integrating a 2-step graph walk kernel with spectral edge regularization bounds. The primary mathematical breakthrough of this work resides in the definitive proof of the Dirichlet Energy Functional Lower-Bound Rigid Clamping Theorem. The derivation demonstrates that under extreme sparseness or zero-degree vacuum conditions, Operator 4 spontaneously activates a global algebraic connectivity scaling valve. This mechanism rigidly restricts the global Dirichlet energy within a strictly positive-definite compact subspace ($\mathcal{E}_D(E_s) \ge \lambda_2(n) \cdot \| E_s \|_2^2 > 0$), thereby neutralizing floating-point truncation singularities and securing the mathematical tractability required for large-scale distributed simulations. By utilizing global prior spectral invariants streamed from the upstream pipeline, Operator 4 effectively disengages physical coherence across disconnected cuts while clamping the algorithmic overhead to a strictly localized $O(1)$ runtime. The engineering feasibility of this operator is validated through a non-local, un-synchronized matrix implementation.
Authors
- Yue Lu (ORCID: https://orcid.org/0009-0008-3405-9170)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-07-28
- DOI
- https://doi.org/10.5281/zenodo.21634865
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00