Spectral Kernel Dynamics for Planetary Surface Graphs: Distinction Dynamics and Topological Conservation
- URL: http://arxiv.org/abs/2604.20887v1
- Date: Fri, 17 Apr 2026 23:38:56 GMT
- Title: Spectral Kernel Dynamics for Planetary Surface Graphs: Distinction Dynamics and Topological Conservation
- Abstract summary: We formalise the distinction dynamics equation dc/dt = G[c, h_t], with MaxCal-optimal realisation G_opt.<n>On fixed-topology 3D surface graphs we derive a conditional topology-preserving compression theorem.<n>A triple necessary spectral diagnostic is derived for planetary drainage networks at O(N) cost.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The spectral kernel field equation R[k] = T[k] lacks a conservation-law analog. We prove (i) the fixed-point flow is strictly volume-expanding (tr DF > 0), precluding automatic conservation, and (ii) the conservation deficit per mode equals the Hessian stability margin exactly: D_m = -Delta'. Closing the deficit requires a scene-side compensating contribution, which we formalise as the distinction dynamics equation dc/dt = G[c, h_t], with MaxCal-optimal realisation G_opt. On fixed-topology 3D surface graphs we derive a conditional topology-preserving compression theorem: retaining k >= beta_0 + beta_1 modes (under a spectral-ordering assumption) preserves all Betti-number charges; we include a worked short-cycle counterexample (figure-eight) calibrating when the assumption fails. A triple necessary spectral diagnostic -- Fiedler-mode concentration, elevated curl energy, anomalous beta_1 -- is derived for planetary drainage networks at O(N) cost. Two internal real-data sequences serve as preliminary consistency checks; full benchmarks and adaptive-topology extensions are deferred.
Related papers
- Optimization Geometrodynamics: Variational Reduction and Interaction Curvature [0.0]
We develop geometrodynamics as a variational theory of this hidden determinant pushforward.<n>For affine pre-reductions, the induced interaction curvature is the negative-semidefinite operator $-G*H-1G$.<n>We prove a global analytic bundle with closed totally geodesic fibers and a unique analytic nearest-controller section.
arXiv Detail & Related papers (2026-07-07T18:42:00Z) - A Spectral Theory of Normalized Corrected GNN Propagation [2.9801823421431854]
We develop a spectral theory for emphnormalized corrected GNN propagation.<n>The central theoretical question is whether this corrected normalized operator preserves class-discnative signal after many propagation layers.<n>We also establish a multi-class partial recovery theorem showing contraction toward class centers for most nodes.
arXiv Detail & Related papers (2026-06-22T16:38:50Z) - Spectral Kernel Dynamics via Maximum Caliber: Fixed Points, Geodesics, and Phase Transitions [0.0]
We derive a closed-form geometric functional for kernel dynamics on finite graphs by applying the Maximum Caliber (MaxCal) variational principle.<n>The framework is grounded in a structural analogy with Einstein's field equations, used as a guiding template rather than an established equivalence.
arXiv Detail & Related papers (2026-04-10T06:22:17Z) - Conservation Law Breaking at the Edge of Stability: A Spectral Theory of Non-Convex Neural Network Optimization [0.0]
We show that gradient descent reliably finds good solutions in non-dimensional network optimization.<n>We identify two regimes separated by a width-dependent dynamical transition.<n>All predictions are validated across 23 experiments.
arXiv Detail & Related papers (2026-04-08T10:41:24Z) - Grokking as a Variance-Limited Phase Transition: Spectral Gating and the Epsilon-Stability Threshold [0.0]
Generalization requires the textitanisotropic rectification unique to adaptives, which directs noise into the tangent space of the solution.<n>This work analyzes AdamW dynamics on modular arithmetic tasks, revealing a Spectral Gating'' mechanism that regulates the transition from generalization to memorization.
arXiv Detail & Related papers (2026-03-16T16:18:18Z) - Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective [33.2175112734242]
Generative Modeling via Drifting has recently achieved state-of-the-art one-step image generation through a kernel-based drift operator.<n>We make the following observation: emphunder a Gaussian kernel, the drift operator is exactly a score difference on smoothed distributions.
arXiv Detail & Related papers (2026-03-10T17:30:35Z) - Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphs [55.77845440440496]
Push-based decentralized communication enables optimization over communication networks, where information exchange may be asymmetric.<n>We develop a unified uniform-stability framework for the Gradient Push (SGP) algorithm.<n>A key technical ingredient is an imbalance-aware generalization bound through two quantities.
arXiv Detail & Related papers (2026-02-24T05:32:03Z) - Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference [1.7523718031184992]
We identify a fundamental mechanism for this failure: textbfPremature Mode Collapse.<n>We propose textbfEfficient Piecewise Hybrid Adaptive Stability Control (EPH-ASC), an adaptive scheduling algorithm that monitors the stability of the inference process.
arXiv Detail & Related papers (2026-01-30T14:47:18Z) - Exact dynamics of quantum dissipative $XX$ models: Wannier-Stark localization in the fragmented operator space [49.1574468325115]
We find an exceptional point at a critical dissipation strength that separates oscillating and non-oscillating decay.
We also describe a different type of dissipation that leads to a single decay mode in the whole operator subspace.
arXiv Detail & Related papers (2024-05-27T16:11:39Z) - Implicit Bias of Gradient Descent for Logistic Regression at the Edge of
Stability [69.01076284478151]
In machine learning optimization, gradient descent (GD) often operates at the edge of stability (EoS)
This paper studies the convergence and implicit bias of constant-stepsize GD for logistic regression on linearly separable data in the EoS regime.
arXiv Detail & Related papers (2023-05-19T16:24:47Z) - On the Double Descent of Random Features Models Trained with SGD [78.0918823643911]
We study properties of random features (RF) regression in high dimensions optimized by gradient descent (SGD)
We derive precise non-asymptotic error bounds of RF regression under both constant and adaptive step-size SGD setting.
We observe the double descent phenomenon both theoretically and empirically.
arXiv Detail & Related papers (2021-10-13T17:47:39Z) - Optimal policy evaluation using kernel-based temporal difference methods [78.83926562536791]
We use kernel Hilbert spaces for estimating the value function of an infinite-horizon discounted Markov reward process.
We derive a non-asymptotic upper bound on the error with explicit dependence on the eigenvalues of the associated kernel operator.
We prove minimax lower bounds over sub-classes of MRPs.
arXiv Detail & Related papers (2021-09-24T14:48:20Z) - Faster Convergence of Stochastic Gradient Langevin Dynamics for
Non-Log-Concave Sampling [110.88857917726276]
We provide a new convergence analysis of gradient Langevin dynamics (SGLD) for sampling from a class of distributions that can be non-log-concave.
At the core of our approach is a novel conductance analysis of SGLD using an auxiliary time-reversible Markov Chain.
arXiv Detail & Related papers (2020-10-19T15:23:18Z) - On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and
Non-Asymptotic Concentration [115.1954841020189]
We study the inequality and non-asymptotic properties of approximation procedures with Polyak-Ruppert averaging.
We prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity.
arXiv Detail & Related papers (2020-04-09T17:54:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.