math.RTSep 3, 2026

What is Smoothness?

Authors: Zachary P Bradshaw

Organizations: QodeX Quantum

Abstract

Smoothness of a function on the real line is reflected in the decay of its Fourier transform, which suggests that smoothness of a function in L2(G)L^2(G) for a group GG should mean concentration of the Fourier coefficients at low frequency. Such a reading presupposes an ordering of the irreducible representations of GG, but for non-abelian GG, no ordering is canonical. Given a symmetric generating set SS, the Laplacian of the associated Cayley graph is block diagonal over the dual, and we order the irreps by the mean of the eigenvalues in each block. This produces an ordering function ω:G^Rω:\widehat{G}\to\mathbb{R} that depends only on the pair (G,S)(G,S). This function is bounded between zero and two, vanishing only at the trivial representation and achieving the upper bound exactly when the Cayley graph is bipartite. We then ask how much freedom the construction has. Within the class of operators satisfying natural axioms, the induced orderings are exactly the real functions on the dual vanishing at the trivial representation and agreeing on conjugate pairs, and the orderings coming from inversion orbits of conjugacy classes form a basis for them. We cut the freedom down further by requiring two additional inputs: nonnegativity of the class weights and a declaration of which group elements count as uniform incremental changes, which pins the operator to the Cayley-Laplacian up to positive scale. We observe that the construction persists for compact groups even though the Cayley graph does not, and we extend the theory to finite sets carrying a transitive group action, where the acting group selects which frequencies exist and the generating set orders them. The answer to the title question is therefore that smoothness is a property of a function together with a choice of group and generating set, not of the function alone.

Explore similar work

Jul 13, 2026stat.ML

Learning the Graphical Nature of Symmetries

Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned. In this paper, a dataset of 131,406131{,}406 Cayley graphs is constructed, covering all groups of order at most 767767 except order 512512, recording exact algebraic labels for group properties together with a broad collection of graph, cycle, distance, and spectral statistics. This census aims to provide novel benchmarks for studying how finite-group properties are reflected in Cayley graph observables. It also yields new enumerative contributions: alongside recovering known OEIS sequences for standard group classes, new sequences for monolithic groups and for groups generated by at most three, four, and five elements are contributed to the OEIS. The accompanying network analysis identifies several empirical regularities and formulates testable conjectures, including relationships involving square clustering, Cayley graph diameter, average graph disorder, and spectral eigengaps of nilpotent groups. Finally, a comparison between classical models, an MLP, and graph neural network architectures is performed for predicting algebraic group properties directly from Cayley graph data. The results show that engineered graph statistics are highly informative, while GNNs, especially GIN and in some fixed-order settings GCN, can recover substantial structural signal directly from the graph. Such that graph-aware architectures show phases of optimality on these group-theoretic graph representations.
Rashid Barket, Enrico Grimaldi, Yacoub Hendi +3
Mar 26, 2025cs.CV

Reconstructing Rational Functions on Finite Abelian Groups with Higher Autocorrelations

The higher-order autocorrelations of integer-valued or rational-valued functions on finite Abelian groups appear naturally in X-ray crystallography, and have applications in computer vision systems, correlation tomography, correlation spectroscopy, and pattern recognition. In this paper, we consider the problem of reconstructing a rational-valued function on finite Abelian groups from its higher-order autocorrelations. We describe an explicit reconstruction algorithm, and prove that the autocorrelations up to order 3r+33r+3 are always sufficient to determine the data up to translation, where rr is the rank of the group. We also provide examples of rational-valued functions on finite Abelian group which are not determined by their autocorrelations up to order 3r+23r+2. In particular, we provide a sharp upper bound on the separating degree of the regular representation of a finite Abelian group in terms of its rank.
W. Riley Casper, Bobby Orozco
Jun 3, 2026cs.LG

Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization

We study the deterministic first-order oracle complexity of finding εε-stationary points in smooth nonconvex optimization when the objective satisfies higher-order smoothness assumptions. While the classical ε2ε^{-2} rate is optimal under only Lipschitz gradients, higher-order smoothness leads to accelerated first-order upper bounds, most notably the ε7/4ε^{-7/4} rate under Lipschitz Hessians and the ε5/3ε^{-5/3} rate under Lipschitz third derivatives. The matching lower bounds, however, have remained open. We resolve this gap by proving a new dimension-free first-order lower bound for higher-order smooth nonconvex functions, valid for every finite smoothness order. In particular, our construction gives a matching Ω(ε7/4)Ω(ε^{-7/4}) lower bound in the Hessian-Lipschitz case and a matching Ω(ε5/3)Ω(ε^{-5/3}) lower bound in the third-order-smooth regime. The hard instance is based on a \emph{block-chain} mechanism that enforces blockwise oracle revelation while preserving the smoothness structure needed for the scalar hard instance. The lower-bound construction was discovered with the assistance of ChatGPT 5.5 Pro and subsequently verified by the authors.
Dongruo Zhou