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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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72144215287 · Jun 202019922001200920172026
48 results for Sobolev scale

The study improves Poincaré and log-Sobolev inequalities on hyperbolic spaces.

problem Improving Poincaré and log-Sobolev inequalities on hyperbolic spaces.
method Establishing scale-dependent Poincaré-Hardy type identities and choosing suitable parameters, potentials, and vector fields.
result Derives new versions and substantially improves existing inequalities.

Optimal multiscale learning of linear operators

problem Statistical and computational limits of learning bounded linear operators between Sobolev spaces
method Reformulate as an infinite-dimensional matrix regression problem with heterogeneous multiscale structure
result Establish minimax rates and construct a finite-resolution blockwise least-squares estimator attaining these rates

Novel algorithm speeds up computation of Sobolev IPM for graph-based probability measures.

problem Efficient computation of Sobolev IPM for graph-based probability measures.
method Established relation between Sobolev norm and weighted LpL^p-norm, proposed novel regularization, leveraged graph structure.
result Proposed regularized Sobolev IPM provides closed-form expression for fast computation.

Scalable kernel methods for large datasets using Fourier representations and NUFFT.

problem Cubic complexity in kernel methods limits their use on large-scale datasets.
method Fourier representation of kernels combined with NUFFT for O(n log n) complexity.
result Achieves minimax convergence rates and processes up to tens of billions of samples.

Sharp log-Sobolev inequalities proved for CD(0,N){\sf CD}(0,N) spaces.

problem Proving log-Sobolev inequalities in noncompact metric measure spaces.
method Sharp isoperimetric inequality, symmetrisation, scaling argument, Hamilton-Jacobi inequality, Sobolev regularity.
result Sharp log-Sobolev inequalities established in CD(0,N){\sf CD}(0,N) spaces.

Develop an ABP approach to Sobolev and Michael-Simon inequalities beyond Euclidean volume growth.

problem Developing an ABP approach to Sobolev and Michael-Simon inequalities under volume noncollapsing assumptions.
method Using a refinement of Brendle's contact-set argument to derive lower bounds for the volumes of geodesic balls.
result A Michael-Simon type inequality for immersed submanifolds with nonnegative sectional curvature and volume noncollapsing.

NO approximates non-Markovian BSDEs with polynomial scaling in 1/ε.

problem Complexity of NO approximations for structured families of BSDEs.
method Identifying structured families of non-Markovian BSDEs, informing NO's inductive bias.
result Polynomial scaling in 1/ε for NO approximations of BSDE solution operators.

Investigates energy minimizers and critical points of scale-invariant tangent-point energies for knots.

problem Finding and characterizing minimizers and critical points of scale-invariant tangent-point energies for closed curves.
method Develops convergence and regularity theories based on fractional Sobolev spaces and new energy functionals.
result Minimizing sequences converge to locally critical embeddings in all but finitely many points, and locally critical embeddings are regular.

Develops local elliptic regularity for geometrically-natural operators with low regularity coefficients.

problem Local elliptic regularity for operators with low regularity coefficients in Sobolev-type spaces.
method Rescaling estimates and multiplication results for function spaces.
result Unified set of interior estimates and regularity inference for operators with Sobolev-type coefficients.

We study some basic analytic questions related to differential operators on Lie manifolds, which are manifolds whose large scale geometry can be described by a a Lie algebra of vector fields on a compactification. We extend to Lie manifolds several classical results on Sobolev spaces, elliptic regularity, and mapping p…

2004-02-19abs ↗pdf ↗

Develops a new method for neural network significance testing without strict constraints.

problem Testing neural networks without bounded weights or specific architectural constraints.
method Uses Rademacher complexity bounds, weakened Sobolev space membership conditions, and a modified sieve space construction.
result Achieves optimal convergence rates and valid asymptotic distributions for test statistics.

Proves existence of solutions with concentrated energy in 2+1 spacetime.

problem Existence of solutions with concentrated energy in 2+1 spacetime.
method Direct treatment of 2+1 Einstein equations, novel scaling, Klainerman-Sobolev inequality.
result Uniform finite-time existence of solutions with positive incoming H1H^1 energy.

Study on Brownian motion on discrete curve spaces, proving stochastic completeness.

problem Analyzing Brownian motion on spaces of discrete curves.
method Introduced and studied Brownian motion on spaces of discrete regular curves with Sobolev-type metrics.
result All geodesically complete spaces of discrete regular curves are stochastically complete.

Smooth activations enable optimal error rates in neural networks for Sobolev function classes.

problem Achieving optimal approximation and estimation error rates for neural networks in Sobolev function classes.
method Study of neural networks with smooth activations, proving optimal rates via approximation and statistical properties.
result Constant-depth networks with smooth activations achieve optimal rates of approximation and estimation, demonstrating smoothness adaptivity.

Proves harmonic coordinates for weak immersions in even dimensions.

problem Existence of harmonic coordinates for weak immersions in Sobolev spaces.
method Analyzes weak immersions in critical Sobolev spaces and uses smallness conditions on the second fundamental form.
result Global harmonic coordinates exist for weak immersions in even dimensions under certain conditions.

Study non-negative curvature Markov chains, proving entropy contraction.

problem Prove entropy contraction for Markov chains with non-negative curvature.
method Prove 1-step contraction in Wasserstein distance implies 1-step contraction in relative entropy.
result Prove MLSI with constant equal to minimal rate increment for mean-field zero-range process.

We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic) restricted to a Sobolev ball defined with respect to a dominant measure μμ. We show that the Sobolev IPM compares two distributions in hig…

2017-11-14abs ↗pdf ↗

Explains optimal functional inequalities, focusing on Sobolev and fractional Sobolev.

problem Optimal functional inequalities and their stability.
method Compactness theorems, characterization of optimizers, and quantitative stability analysis.
result Characterization and stability of optimizers for Sobolev inequalities and their fractional generalizations.

Optimal rates for vector-valued regression on various norms.

problem Optimal rates for vector-valued ridge regression on continuous norms.
method Combining standard capacity assumptions with tensor product constructions of vector-valued interpolation spaces.
result Optimal rates for vector-valued ridge regression, independent of output space dimension.

Transformer networks approximate Hölder and Sobolev functions with fixed-depth networks.

problem Nonparametric regression with dependent observations.
method Established novel upper bounds for Transformer networks approximating Hölder and Sobolev functions under various ββ-mixing data assumptions.
result Explicit convergence rates for nonparametric regression problems under ββ-mixing data assumptions.

Log Sobolev and Michael Simon inequalities for tensor fields on curved manifolds.

problem Establishing inequalities for tensor fields on curved manifolds.
method Applying the ABP method to symmetric tensor fields on manifolds with nonnegative sectional curvature.
result Log Sobolev and Michael Simon inequalities for tensor fields.

Stochastic Gradient Descent improved for various Hilbert scales and misspecified models.

problem Understanding and optimizing SGD in Hilbert scales for machine learning.
method Extending SGD analysis to Hilbert scales, including Sobolev and Diffusion spaces, and showing the effects of smoothness and preconditioning.
result Violation of smoothness assumption affects learning rate; preconditioning in Hilbert scales reduces the number of iterations for misspecified models.

Stability of singularity formation in Yang-Mills fields in higher dimensions.

problem Stability of self-similar blowup profiles for Yang-Mills equations in (1+d)(1+d)-dimensions.
method Analysis of explicitly known equivariant self-similar blowup solution and small equivariant perturbations.
result Global-in-space asymptotic stability of the self-similar blowup solution for Yang-Mills equations in (1+d)(1+d)-dimensions for d5d \geq 5.

In this paper we study the problem of deriving further Sobolev inequalities from a given Sobolev inequality. We use several different methods, including Bessel potentials and Riesz transforms. We apply the results to the Ricci flow to extend the author's results on the W1,2W^{1,2} Sobolev inequality along the Ricci flow …

2007-09-04abs ↗pdf ↗

Proves Sobolev inequality on manifolds with specific curvature properties.

problem Proving Sobolev inequality on manifolds with asymptotically nonnegative Bakry-Émery Ricci curvature.
method Density and Bakry-Émery Ricci curvature.
result Proves Sobolev inequality on manifolds with asymptotically nonnegative Bakry-Émery Ricci curvature.

Paper connects Fenchel-Willmore and Sobolev inequalities for submanifolds in curved spaces.

problem Developing inequalities for submanifolds in curved spaces.
method Connecting Fenchel-Willmore and logarithmic Sobolev inequalities for mean-convex submanifolds.
result Established extensions of Fenchel-Willmore inequality and derived new Sobolev-type inequalities.

We define the "sum of squares of the wavelengths" of a Riemannian surface (M,g) to be the regularized trace of the inverse of the Laplacian. We normalize by scaling and adding a constant, to obtain a "mass", which is scale invariant and vanishes at the round sphere. This is an anlaog for closed surfaces of the ADM mass…

2008-10-03abs ↗pdf ↗

Study shows how close functions are to optimal in Riemannian manifolds.

problem Understanding how close functions are to optimal in Riemannian manifolds.
method Analyzes quantitative stability of Sobolev inequalities on compact Riemannian manifolds.
result Functions that nearly saturate a critical Sobolev inequality are quantitatively close to extremal functions.

The paper examines stability of Sobolev inequalities on manifolds with Ricci curvature bounds.

problem Stability of Sobolev inequalities on Riemannian manifolds with Ricci curvature lower bounds.
method Generalized Lions' concentration compactness and rigidity results of Sobolev inequalities on singular spaces.
result Almost extremal functions are close to extremal functions on the round sphere and Euclidean Sobolev inequality.

Generalizes Sobolev IPM for graph-based measures using Orlicz geometric structure.

problem Limitation of Le et al. (2025) framework to LpL^p geometry.
method Generalizes Sobolev IPM through Orlicz geometric structure, employing convex functions to capture nuanced geometric relationships.
result GSI-M reduces to a simple univariate optimization problem, achieving remarkable computational efficiency.

Multivariate splines linked to infinitely-wide neural networks with improved numerical performance.

problem Understanding the relationship between multivariate splines and neural networks.
method Showed multivariate splines can be represented as random features in infinitely-wide neural networks with a homogeneous activation function.
result The function space of multivariate splines is a Sobolev space on a Euclidean ball with explicit norm bounds on derivatives.