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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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115229344458 · Jun 202019922001200920172026
48 results for analytic bounds

Call a smooth knot (or smooth link) in the unit sphere in C2\mathbb{C}^2 analytic (respectively, smoothly analytic) if it bounds a complex curve (respectively, a smooth complex curve) in the complex ball. Let KK be a smoothly analytic knot. For a small tubular neighbourhood of KK we give a sharp lower bound for the 4…

2015-04-24abs ↗pdf ↗

New method to parametrize infinite Riemann surfaces with bounded triangulations.

problem Parametrizing infinite Riemann surfaces with bounded triangulations.
method Introducing bounded ideal triangulations and proving real-analyticity of the parametrization.
result Real-analytic parametrization of Teichmüller spaces for infinite surfaces with bounded triangulations.

Analytic networks with bounded coefficients can't outperform polynomial approximations.

problem Approximation limits of neural networks with analytic activation functions under coefficient constraints.
method Deterministic analysis using comparison argument and Bernstein-type estimates.
result Networks with analytic activation functions and controlled coefficients cannot outperform classical polynomial approximation rates on non-analytic targets.

Paper proves all Lagrangians unobstructed if one is, using non-archimedean analytic structure.

problem Proving the existence of bounding cochains for unobstructed Lagrangians.
method Introducing non-archimedean analytic structure and using family Floer techniques.
result All Lagrangians in a connected family are unobstructed if one is.

Wide neural networks can learn complex functions like gravitational force law.

problem Learning complex functions like gravitational force law with neural networks.
method Extending theoretical bounds to analytic functions on the sphere using SGD and ReLU networks.
result Wide ReLU networks can learn analytic functions efficiently with proportional number of samples.

The paper calculates bounds on the local Lipschitz constants of neural network layers.

problem Understanding the Lipschitz constants of neural network layers for robustness analysis.
method Analytical approach to determine upper bounds on local Lipschitz constants of affine-ReLU functions.
result The method produces tighter bounds than the standard conservative bound, especially for small perturbations.

We construct a continuous time model for price-mediated contagion precipitated by a common exogenous stress to the banking book of all firms in the financial system. In this setting, firms are constrained so as to satisfy a risk-weight based capital ratio requirement. We use this model to find analytical bounds on the …

2018-07-07abs ↗pdf ↗

We prove the analyticity in time for solutions of two parabolic equations in the whole space, without any decaying or vanishing conditions. One of them involves solutions to the heat equation of exponential growth of order 22 on $\M$. Here $\M$ is Rd\R^d or a complete noncompact manifold with Ricci curvature bounded f…

2019-07-03abs ↗pdf ↗

New Hessian estimators for Riemannian manifolds with reduced bias.

problem Estimating Hessians on Riemannian manifolds with reduced bias and computational efficiency.
method Introducing new stochastic zeroth-order Hessian estimators using O(1)O(1) function evaluations.
result Achieved a bias bound of order O(γδ2)O(γδ^2) for analytic real-valued functions.

New bounds for quantile aggregation unify and clarify existing methods.

problem Analytical bounds for quantile aggregation with dependence uncertainty.
method Using inf-convolution of quantile-based risk measures, establish new analytical bounds called convolution bounds.
result Convolution bounds are the best available and provide sharp results in many cases.

Let p and l be two distinct prime numbers and let G be a group. We study the asymptotic behaviour of the mod-l Betti numbers in p-adic analytic towers of finite index subgroups. If X is a finite l-group of automorphisms of G, our main theorem allows to lift lower bounds for the mod-l cohomology growth in the fixed poin…

2013-05-22abs ↗pdf ↗

Given a choice of metric on the Riemann surface, the regularized determinant of Laplacian (analytic torsion) is defined via the complex power of elliptic operators: det(Δ)=exp(ζ(0)) \det(Δ)=\exp(-ζ'(0)) In this paper we gave an asymptotic effective estimate of analytic torsion under Arakelov metric. In particular, after taking th…

2019-03-20abs ↗pdf ↗

Torsion invariants for manifolds which are not simply connected were introduced by K. Reidemeister and generalized to higher dimensions by W. Franz. The Reidemeister torsion, was the first invariant of manifolds which was not a homotopy invariant. The analytic counterpart of the combinatorial Reidemeister torsion was i…

2008-08-04abs ↗pdf ↗

Extends machine learning models for analytic boundary conditions in differential equations.

problem Inclusion of data in differential equations using symbolic algorithms.
method Combines computer algebra with Gaussian processes and extends to analytic boundary conditions using Gröbner and Janet bases of Weyl algebras.
result Describes divergence-free flow in domains bounded by analytic functions.

We prove that smooth critical points of the Möbius energy parametrized by arc-length are analytic. Together with the main result in \cite{BRS16} this implies that critical points of the Möbius energy with merely bounded energy are not only CC^\infty but also analytic. Our proof is based on Cauchy's method of majorants…

2018-05-15abs ↗pdf ↗

It is well known that generic solutions of the heat equation are not analytic in time in general. Here it is proven that ancient solutions with exponential growth are analytic in time in ${\M} \times (-\infty, 0]$. Here $\M=\R^n$ or is a manifold with Ricci curvature bounded from below. Consequently a necessary and suf…

2019-05-14abs ↗pdf ↗

The paper calculates upper bounds on ReLU network Lipschitz constants.

problem Determining the maximum perturbation size for robustness of neural networks.
method Analyzing ReLU, affine-ReLU, and max pooling functions; combining results; tracking zero elements; using a computational approach.
result The method produces the largest known bounds on minimum adversarial perturbations for large networks.

The paper proves a conjecture about the Bergman metric of real analytic domains.

problem Proving the Cheng-Yau conjecture for real analytic pseudoconvex domains.
method Localization of Bergman kernels, extension theorem, and Einstein metrics.
result The Bergman metric of a bounded pseudoconvex domain with real-analytic boundary is Einstein if and only if the domain is biholomorphic to the unit ball.

Study on Einstein solitons with bounds and asymptotic behavior.

problem Understanding the properties of Einstein solitons.
method Computed lower bounds for scalar curvature, established asymptotic behavior, proved finiteness of fundamental group and weighted volume.
result Established finiteness of fundamental group and weighted volume for gradient shrinking Einstein solitons.

The Kähler-Ricci flow near conical singularities is described with a C/tC/t curvature bound.

problem Describing the Kähler-Ricci flow near conical singularities.
method Showed a C/tC/t curvature bound and used the unique Kähler-Ricci expander.
result The flow near each singular point is modelled on the unique Kähler-Ricci expander.

Let ΩR2 Ω\subset R^2 be a bounded piecewise smooth domain and φλφ_λ be a Neumann (or Dirichlet) eigenfunction with eigenvalue λ2λ^2 and nodal set Nφλ=xΩ;φλ(x)=0.{ N}_{φ_λ} = {x \in Ω; φ_λ(x) = 0}. Let HΩH \subset Ω be an interior CωC^ω curve. Consider the intersection number n(λ,H):=#(HNφλ). n(λ,H):= \# (H \cap N_{φ_λ} ). We first prove that fo…

2012-11-14abs ↗pdf ↗

Deep neural networks approximate analytic functions in high dimensions with exponential rates.

problem Approximating analytic functions in high-dimensional spaces using neural networks.
method Analyzing convergence rates of ReLU and ReLU^k activations in L2(Rd,γd)L^2(\mathbb{R}^d,γ_d) for dN{}d\in\mathbb{N}\cup\{\infty\}.
result Exponential convergence rates for analytic functions in L2(Rd,γd)L^2(\mathbb{R}^d,γ_d) for dNd\in\mathbb{N}, and dimension-independent bounds for d=d=\infty.

Geodesically complete spaces with curvature bounded above have maps with finite energy that are Lipschitz.

problem Analyzing the properties of geodesically complete spaces with curvature constraints.
method Geometric perturbations of geodesics to curves with zero length on singular sets.
result Every Sobolev map in W1,W^{1,\infty} space has a Lipschitz representative with the same Lipschitz constant as its infinity energy.

Paper improves MMD estimation for analytical mean embeddings.

problem Improving MMD estimation for distributions with analytical mean embeddings.
method Proposes a tighter concentration result for MMD estimation under semi-explicit settings and extends to unbounded kernels.
result Demonstrates efficiency in real-world applications like index replication and calibration.

Lower bound proves ridgeless regression performs poorly near interpolation threshold.

problem Proving performance of ridgeless regression near interpolation threshold.
method Distribution-independent lower bound for mean squared error in noisy ridgeless linear regression.
result Lower bound implies ridgeless regression performs poorly near interpolation threshold.

Analyticity of heat equation extended to Bakry-Émery Ricci curvature manifolds.

problem Analyticity of solutions to heat equation under specific curvature conditions.
method Analyzes analyticity in time for smooth solutions on Riemannian manifolds with Bakry-Émery Ricci curvature.
result Analyticity extended to all gradient Ricci solitons and certain LpL^p spaces.

Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generat…

2018-05-28abs ↗pdf ↗

New bounds for neural networks ensure robustness and accuracy.

problem Ensuring robustness of neural networks by computing Lipschitz constants.
method Analyzed and proposed new bounds for l1l^1 and ll^\infty norms, using explicit and implicit methods for convnets.
result One of the new bounds is optimal and more accurate than existing ones.

A coupling method and an analytic one allow us to prove new lower bounds for the spectral gap of reversible diffusions on compact manifolds. Those bounds are based on the a notion of curvature of the diffusion, like the coarse Ricci curvature or the Bakry--Emery curvature-dimension inequalities. We show that when this …

2011-05-30abs ↗pdf ↗