arXiv research

Example of non-smooth isotopy using K3 surfaces.

For non-smooth surfaces, the measure of Brownian loops is derived using the Polyakov-Alvarez formula.

We study hyperbolic polyhedral surfaces with faces isometric to regular hyperbolic polygons satisfying that the total angles at vertices are at least $2π.$ The combinatorial information of these surfaces is shown to be identified with that of Euclidean polyhedral surfaces with negative combinatorial curvature everywher…

Lipschitz maps on metric surfaces are rigid if they preserve area.

MARINA-P improves non-smooth federated optimization with adaptive stepsizes.

Extends curve theory to non-smooth data with finite curvature and torsion.

AsylADMM improves gossip-based learning for non-smooth objectives.

Survey on preserving curvature bounds for non-smooth Ricci flow.

We introduce non-smooth symplectic forms on manifolds and describe corresponding Poisson structures on the algebra of Colombeau generalized functions. This is achieved by establishing an extension of the classical map of smooth functions to Hamiltonian vector fields to the setting of non-smooth geometry. For mildly sin…

Study particle dynamics in non-differentiable fractal spaces.

New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.

The paper explores various stationarity concepts in non-smooth optimization.

We consider the problem of finding local minimizers in non-convex and non-smooth optimization. Under the assumption of strict saddle points, positive results have been derived for first-order methods. We present the first known results for the non-smooth case, which requires different analysis and a different algorithm…

Smoothness analysis of adversarial training reveals $L_\infty$ constraints cause more non-smoothness.

An anisotropic surface energy is the integral of an energy density that depends on the normal at each point over the considered surface, and it is a generalization of surface area. The minimizer of such an energy among all closed surfaces enclosing the same volume is unique and it is (up to rescaling) so-called the Wul…

In the framework of Lorentzian warped products, we study the Friedmann-Robertson-Walker cosmological model to investigate non-smooth curvatures associated with multiple discontinuities involved in the evolution of the universe. In particular we analyze non-smooth features of the spatially flat Friedmann-Robertson-Walke…

This work speeds up hyperparameter selection for non-smooth convex models using implicit differentiation.

New algorithms optimize non-smooth, non-convex objectives with improved complexity.

In this paper, we develop a novel {\bf ho}moto{\bf p}y {\bf s}moothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute. The best known iteration compl…

We give a metric characterization of the scalar curvature of a smooth Riemannian manifold, analyzing the maximal distance between $(n+1)$ points in infinitesimally small neighborhoods of a point. Since this characterization is purely in terms of the distance function, it could be used to approach the problem of definin…

Positive mass theorem for non-smooth metrics on flat manifolds with corners.

Paper tackles private optimization for non-smooth objectives efficiently.

We investigate a generalization of the so-called metric splitting of globally hyperbolic space-times to non-smooth Lorentzian manifolds and show the existence of this metric splitting for a class of wave-type space-times. Our approach is based on smooth approximations of non-smooth space-times by families (or sequences…

Advances smooth over-parameterization for solving non-smooth optimization problems.

New methods improve convergence in non-convex non-smooth learning problems.

New gauge theory invariant detects non-smooth isotopy of $P^2$-knots.

The study analyzes convergence rates for sparse pivotal estimators in high-dimensional regression.

Adaptive data fusion boosts efficiency in multi-task optimization.

Algebras of generalized functions offer possibilities beyond the purely distributional approach in modelling singular quantities in non-smooth differential geometry. This article presents an introductory survey of recent developments in this field and highlights some applications in mathematical physics.

Abstracts a theorem for non-smooth maps in infinite dimensions.

New SPS variant improves non-smooth optimization without small gradients.

An extrinsic representation of a Ricci flow on a differentiable n-manifold M is a family of submanifolds S(t), each smoothly embedded in R^{n+k}, evolving as a function of time t such that the metrics induced on the submanifolds S(t) by the ambient Euclidean metric yield the Ricci flow on M. When does such a representa…

Stochastic approximation proves asymptotic normality for non-smooth problems.

Bayesian optimization tackles non-smooth tuning problems.

We provide improved convergence rates for various \emph{non-smooth} optimization problems via higher-order accelerated methods. In the case of $\ell_\infty$ regression, we achieves an $O(ε^{-4/5})$ iteration complexity, breaking the $O(ε^{-1})$ barrier so far present for previous methods. We arrive at a similar rate fo…

Safe-EF improves federated learning for non-smooth, constrained optimization.

We theoretically discuss why deep neural networks (DNNs) performs better than other models in some cases by investigating statistical properties of DNNs for non-smooth functions. While DNNs have empirically shown higher performance than other standard methods, understanding its mechanism is still a challenging problem.…

Modified perturbation method removes non-smoothness in solving Black-Scholes equations.

The study extends curvature bounds to non-smooth spaces and proves stability of mean curvature.

Expanding FCCO to non-smooth weakly-convex problems, improving deep learning performance.

New approach confirms Kruskal-Szekeres extension for Schwarzschild spacetime.

Novel method for shape optimization of non-smooth PDEs.

Injectivity of X-ray transform proven for non-smooth metrics.

Timelike curvature and Brunn-Minkowski inequality linked in non-smooth spacetimes.

Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object…

New sampling algorithm for non-smooth potentials.

New method tackles non-smooth tensor data for better recovery.

Optimal private ERM and SCO with subquadratic gradient complexity.