Optimization Geometry of Equivalent Brownian RKHS Representations

2026-09-18

AI Summary

研究了在有限布朗RKHS中不同坐标表示对优化算法的影响，通过节点、增量和频谱坐标分析，揭示了相同初始化下GD/SGD轨迹的一致性及增量GD的特殊性质。

Abstract

Equivalent finite parameterizations can represent the same functions and intrinsic norm yet induce different optimization algorithms. We study this effect in a controlled finite Brownian RKHS with nodal, increment, and spectral coordinates. Classical finite-element, RKHS-interpolation, Brownian-covariance, and mixed-boundary DCT identities make the shared hypothesis class, Brownian energy, approximation operator, and coordinate maps explicit. Our main results concern the optimization geometry of this fixed model. With mapped initialization, identical scalar steps, and identical minibatches, nodal and spectral GD/SGD have exactly the same mapped trajectories. Increment GD is an explicit Euler step for the constant Brownian/Sobolev metric, with factor $1/h$. For Brownian-regularized least squares, $κ_2(\mathbf H_{\mathrm{inc}})\le1+A/ρ$, independently of grid resolution $G$ for fixed $A$, $ρ>0$, and the stated normalization. Under the stated standard-Adam convention, the universal orthogonal equivariance group is exactly the signed permutations; the block DCT-VIII transform is not one. Float64 tests over five grids numerically verify the finite identities, mapped one-layer and recursive trajectories, conditioning predictions, and theorem-matched Adam separation. Thus coordinate effects are isolated without changing the represented functions, intrinsic regularizer, or approximation space.

Problem

• Brownian RKHS
• optimization geometry
• coordinate representations
• equivalent parameterizations
• finite element

Innovation

• Optimization Geometry
• Brownian RKHS
• Coordinate Effects
• GD/SGD Trajectories
• Euler Step