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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.

169,051 papers · 148 categories

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12.5%25.0%37.5%50.0% · Sep 199319922001200920182026
48 results for sharp trace

Derives a sharp inequality for trace-free matrices with applications to hypersurfaces.

problem Classifying conformally flat hypersurfaces and characterizing rotational hypersurfaces.
method Derives a sharp inequality relating eigenvalues of trace-free matrices and applies it to hypersurfaces.
result New proof of the classification of conformally flat hypersurfaces and construction of a functional for rotational hypersurfaces.

Derives sharp Sobolev trace inequalities for higher order derivatives on balls and half-spaces.

problem Sharp Sobolev trace inequalities for higher order derivatives.
method Scattering theory on hyperbolic spaces, generalized Poisson kernel, explicit formulas of extremal functions.
result Explicit formulas of extremal functions and sharp trace Sobolev inequalities.

The paper introduces boundary operators for Poincaré-Einstein manifolds and proves higher order trace inequalities.

problem Proving higher order trace inequalities on Poincaré-Einstein manifolds.
method Introducing conformally covariant boundary operators and using them to set up higher order Dirichlet problems.
result Sharp higher order Sobolev trace inequalities on the ball are obtained.

Differentiable relaxation for inferring partial orders from noisy linear data.

problem Inference of partial orders from linear data with noisy observations.
method Introducing a differentiable relaxation to model noisy linear extensions, replacing discontinuous precedence and feasibility with smooth surrogates.
result Smooth posterior that preserves partial-order semantics, supports gradient-based inference, and converges to hard likelihood.

We prove a linear trace Li-Yau-Hamilton inequality for the Kaehler-Ricci flow. We then use this sharp differential inequality to study the Liouville properties of the plurisubharmonic functions on complete Kaehler manifolds with nonnegative bisectional curvature.

2002-11-14abs ↗pdf ↗

The study proves Liouville theorems on curved manifolds with convex boundaries.

problem Proving Liouville theorems on manifolds with nonnegative curvature and strictly convex boundary.
method Analyzing smooth compact Riemannian manifolds with nonnegative sectional curvature and strictly convex boundary.
result Derives Liouville theorems and verifies a conjecture about eigenvalues and inequalities.

Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.

problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.

Develops interpolation methods for matrix functions in statistics and machine learning.

problem Estimating matrix functions in statistics and machine learning.
method Interpolates log-determinant and trace of matrix powers using modified sharp bounds.
result Accuracy and performance demonstrated in numerical examples.

New heat trace coefficients reveal curvature effects in polygonal domains.

problem Understanding heat trace behavior in polygonal domains with curved corners.
method Local heat trace expansion through order t1/2t^{1/2}, analyzing both Dirichlet and Neumann boundary conditions.
result Sharp sign law for the Dirichlet angular factor of the first corner-curvature heat invariant.

Study optimizes KSD estimation from samples, revealing Hilbert-Schmidt vs trace scales.

problem Optimizing estimation of Kernel Stein Discrepancy from samples.
method Identifying and comparing minimax scales for U-statistic and V-statistic.
result Hilbert-Schmidt norm of Stein covariance operator gives optimal scale.

The paper introduces new boundary operators and proves higher order CR Sobolev trace inequalities for Siegel domain and complex ball.

problem Establishing higher order CR Sobolev trace inequalities for Siegel domain and complex ball.
method Introducing conformally covariant boundary operators, proving extension theorems, and establishing trace inequalities.
result Generalized CR Sobolev trace inequalities for all γ ∈ (0, n+1) \mathbb{N}.

The density matrices are positively semi-definite Hermitian matrices of unit trace that describe the state of a quantum system. The goal of the paper is to develop minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, i…

2015-07-17abs ↗pdf ↗

We establish sharp Sobolev inequalities of order four on Euclidean d-balls for d greater than or equal to four. When d=4, our inequality generalizes the classical second order Lebedev-Milin inequality on Euclidean 2-balls. Our method relies on the use of scattering theory on hyperbolic d-balls. As an application, we ch…

2015-09-20abs ↗pdf ↗

We describe a set of conformally covariant boundary operators associated to the Paneitz operator, in the sense that they give rise to a conformally covariant energy functional for the Paneitz operator on a compact Riemannian manifold with boundary. These operators naturally give rise to a first- and third-order conform…

2015-09-28abs ↗pdf ↗

Bounds on spectral gaps of hyperbolic 3-manifolds and orbifolds.

problem Constraining the spectra of Laplace operators on hyperbolic manifolds and orbifolds.
method Linear programming and spectral identities derived from the conformal bootstrap and Selberg trace formula.
result Upper bounds on the first and second Laplacian eigenvalues, and spectral gaps of hyperbolic 3-manifolds and orbifolds.

A rather complete investigation of anisotropic Bessel potential, Besov, and Hölder spaces on cylinders over (possibly) noncompact Riemannian manifolds with boundary is carried out. The geometry of the underlying manifold near its 'ends' is determined by a singularity function which leads naturally to the study of weigh…

2012-04-03abs ↗pdf ↗

Different optimizer choices lead to different financial model predictions.

problem The impact of optimizer choice on neural network models in financial time series.
method Analysis of large-scale volatility forecasting for S&P 500 stocks using various model-training-pipeline pairs.
result Optimizer choice reshapes non-linear response profiles and temporal dependence in financial models, leading to different functional outcomes.

The systole of a hyperbolic surface is bounded by a logarithmic function of its genus. This bound is sharp, in that there exist sequences of surfaces with genera tending to infinity that attain logarithmically large systoles. These are constructed by taking congruence covers of arithmetic surfaces. In this article we p…

2015-12-21abs ↗pdf ↗

We describe a set of conformally covariant boundary operators associated to the sixth-order GJMS operator on a conformally invariant class of manifolds which includes compactifications of Poincaré--Einstein manifolds. This yields a conformally covariant energy functional for the sixth-order GJMS operator on such manifo…

2018-10-18abs ↗pdf ↗

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 ↗

Under a spectral assumption on the Laplacian of a Poincaré--Einstein manifold, we establish an energy inequality relating the energy of a fractional GJMS operator of order 2γ(0,2)2γ\in(0,2) or 2γ(2,4)2γ\in(2,4) and the energy of the weighted conformal Laplacian or weighted Paneitz operator, respectively. This spectral assumption…

2015-09-28abs ↗pdf ↗

New theory explains how chaotic training improves neural network generalization.

problem Understanding how chaotic training improves neural network generalization.
method Representing stochastic optimizers as random dynamical systems and introducing a new dimension concept.
result Generalization in chaotic training depends on the complete Hessian spectrum and partial determinants.

The paper explores how Finsler manifolds differ from Riemannian ones in functional inequalities.

problem Analytic phenomena on Finsler manifolds differ from Riemannian ones.
method Comparative analysis of Finsler and Riemannian manifolds, focusing on Sobolev spaces, Hardy inequalities, and uncertainty principles.
result Functional inequalities (Hardy, uncertainty) break down on Finsler Cartan-Hadamard manifolds, while Caffarelli-Kohn-Nirenberg inequality exhibits a sharp threshold.

Formula for heat coefficients of curved conic singularities derived from Riemannian metrics.

problem Calculating heat coefficients for surfaces with curved conic singularities.
method Explicit formula derivation for coefficient b1/2(C)b_{1/2}(C) under rotationally invariant metrics near conical singularities.
result The coefficient b1/2(C)b_{1/2}(C) varies irrationally under constant rescalings near the cone point, contrasting with other coefficients.

Study on quantitative aspects of trace polynomials in free groups.

problem Understanding the exact formula and bounds for trace polynomials in free groups.
method Proved exact formula for leading homogeneous part, obtained sharp bounds, studied random words, and provided deterministic algorithm.
result Sharp bounds on the degree of trace polynomials and growth rates of polynomial sizes.