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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.

168,742 papers · 148 categories

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64127191254 · Jun 202019922001200920172026
48 results for positive definite tensors

The paper defines and studies the geometric mean for tensors and its associated Riemannian geometry.

problem Defining and studying the geometric mean for tensors.
method Generalized geometric mean for tensors using T-product, verified properties, and investigated Riemannian manifold.
result Geometric mean of T-positive definite tensors is a unique solution of algebraic Riccati tensor equations and a midpoint of geodesics.

Study of HH-eigenvalues for complex tensors and their applications in differential geometry.

problem Characterizing HH-eigenvalues of Hermitian tensors.
method Introduced HH-eigenvalues, derived inclusion sets, and established criteria for definiteness.
result Determined inclusion sets and criteria for Hermitian and CPS tensors.

The paper solves a problem related to Higgs bundles and Hermitian metrics.

problem Existence of Hermitian metrics with prescribed Hermitian-Yang-Mills tensors for Higgs bundles.
method Solving the prescribed Hermitian-Yang-Mills tensor problem for Higgs bundles over compact complex manifolds.
result For any Hermitian positive definite tensor, there exists a unique smooth Hermitian metric on the Higgs bundle.

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.

A family of probability distributions parametrized by an open domain ΛΛ in RnR^n defines the Fisher information matrix on this domain which is positive semi-definite. In information geometry the standard assumption has been that the Fisher information matrix tensor is positive definite defining in this way a Riemannia…

2015-03-29abs ↗pdf ↗

On a Hermitian manifold we construct a symmetric (1,1)(1,1)- tensor HH using the torsion and the curvature of the Chern connection. On a compact balanced Hermitian manifold we find necessary and sufficient conditions in terms of the tensor HH for a harmonic 11-form to be analytic and for an analytic 11-form to be harm…

1996-06-24abs ↗pdf ↗

In Lorentzian manifolds of any dimension the concept of causal tensors is introduced. Causal tensors have positivity properties analogous to the so-called ``dominant energy condition''. Further, it is shown how to build, from ANY given tensor AA, a new tensor quadratic in AA and ``positive'', in the sense that it is …

2002-02-20abs ↗pdf ↗

The Riemann curvature tensor is a central mathematical tool in Einstein's theory of general relativity. Its related eigenproblem plays an important role in mathematics and physics. We extend M-eigenvalues for the elasticity tensor to the Riemann curvature tensor. The definition of M-eigenproblem of the Riemann curvatur…

2018-02-28abs ↗pdf ↗

This paper derives radial fields on manifolds of symmetric positive definite matrices.

problem Lack of an expression for radial fields on manifolds of symmetric positive definite matrices.
method Derives an expression for radial fields on manifolds of symmetric positive definite matrices.
result Derives an expression for radial fields on manifolds of symmetric positive definite matrices.

A purely algebraic construction of super-energy tensors for arbitrary fields is presented in any dimensions. These tensors have good mathematical and physical properties, and they can be used in any theory having as basic arena an n-dimensional manifold with a metric of Lorentzian signature. In general, the completely …

1999-06-21abs ↗pdf ↗

For a standard Finsler metric F on a manifold M, its domain is the whole tangent bundle TM and its fundamental tensor g is positive-definite. However, in many cases (for example, the well-known Kropina and Matsumoto metrics), these two conditions are relaxed, obtaining then either a pseudo-Finsler metric (with arbitrar…

2011-11-22abs ↗pdf ↗

As is well known, a metric on a manifold determines a unique symmetric connection for which the metric is parallel: the Levi-Civita connection. In this paper we investigate the inverse problem: to what extent is the metric of a Riemannian manifold determined by its Levi-Civita connection? It is shown that for a generic…

2006-09-26abs ↗pdf ↗

The paper studies modified Einstein tensors and their positivity properties on compact manifolds.

problem Analyzing the positivity of modified Einstein tensors and their implications on compact manifolds.
method Investigates modified Einstein tensors defined as $\Eink :=\Scal \, g -k\Ric$ for 0<k<n0<k<n and studies their positivity properties.
result Defines a smooth invariant $\cEin(M)$ measuring how far a manifold is from admitting an Einstein metric with positive scalar curvature.

We present a new method for online prediction and learning of tensors (NN-way arrays, N>2N >2) from sequential measurements. We focus on the specific case of 3-D tensors and exploit a recently developed framework of structured tensor decompositions proposed in [1]. In this framework it is possible to treat 3-D tensors …

2015-07-28abs ↗pdf ↗

We introduce and study covariance fields of distributions on a Riemannian manifold. At each point on the manifold, covariance is defined to be a symmetric and positive definite (2,0)-tensor. Its product with the metric tensor specifies a linear operator on the respected tangent space. Collectively, these operators form…

2008-07-29abs ↗pdf ↗

A new metric tensor improves Riemann manifold Monte Carlo for Bayesian models.

problem Improving sampling efficiency in Bayesian hierarchical models.
method Metric tensor derived from log-density gradient covariance matrices.
result Metric tensors enhance sampling for complex Bayesian models.

This work tackles regression on non-Euclidean spaces, specifically positive-definite matrices with the Bures-Wasserstein metric.

problem Regression on non-Euclidean spaces, specifically positive-definite matrices with the Bures-Wasserstein metric.
method Developed a sufficient condition for the existence of a minimizer of the conditional barycenter problem, characterized the optimization landscape, and developed a projection-free algorithm for approximate computation of first-order stationary points.
result The objective is free of local maxima under the sufficient condition, and the algorithm enables the use of stochastic Riemannian optimization methods for large-scale setups.

Proves Morse index theorem for geodesics in conic Finsler manifolds.

problem Geodesic index theorem in conic Finsler manifolds with variable endpoints.
method Proves Morse index theorem for geodesics connecting submanifolds in a C7C^7 manifold with a C6C^6 conic pseudo-Finsler metric.
result Establishes the Morse index theorem for geodesics in conic Finsler manifolds.

KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.

problem Multi-way data imputation for high-dimensional functional MRI and dynamic graph recovery.
method Reformulates imputation as RKHS regression with TT-constrained coefficients and Hadamard overparameterization. Optimizes TT coefficients and kernel matrices on Riemannian manifolds.
result Consistently outperforms state-of-the-art methods in modeling accuracy.

KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.

problem Multi-way data imputation in high-dimensional spaces.
method Reformulates imputation as RKHS regression with TT-constrained coefficients, optimized on manifold frameworks.
result Consistently outperforms state-of-the-art methods in accuracy.

As a difference with the positive-definite Riemannian case, in the Lorentzian case there exists proper second-order symmetric spacetimes, i.e., those with vanishing second covariant derivative of the Riemannian tensor (Rλμνρ;α;β=0R_{λμνρ;α;β}=0) which are not locally symmetric (Rλμνρ;α0R_{λμνρ;α}\neq 0). In fact, they lie in the clas…

2010-01-20abs ↗pdf ↗

Matsumoto conjectured that for any Finsler manifold (M,F)(M, F) for which the restriction of the fundamental tensor to the indicatrix of FF is positive definite, the absolute length F(X)F(X) of any tangent vector XTxMX \in T_xM is the global minimum for the relative length Xy|X|_y as yy varies along the indicatrix $I_x \sub…

2018-01-24abs ↗pdf ↗

The geometry of a ball within a Riemannian manifold is coarsely controlled if it has a lower bound on its Ricci curvature and a positive lower bound on its volume. We prove that such coarse local geometric control must persist for a definite amount of time under three-dimensional Ricci flow, and leads to local C/t deca…

2016-11-18abs ↗pdf ↗

Curvature tensors can always be matched to a metric tensor under certain conditions.

problem Sectionally positive curvature tensors and their relationship to metric tensors.
method Existence and uniqueness of a metric tensor gabg_{ab} such that Rabcdgbd=gacλR_{abcd} g^{bd} = g_{ac} λ.
result A metric tensor gabg_{ab} can be found for sectionally positive curvature tensors, and it is unique up to a constant factor.

Vanishing theorem for certain tensor fields on compact Hermitian manifolds.

problem Vanishing theorem for holomorphic tensor fields on compact Hermitian manifolds.
method Inspired by X. Yang and L. Ni-F. Zheng's ideas, the proof uses the definiteness of holomorphic sectional curvature.
result Spaces of certain holomorphic tensor fields are trivial under the definiteness of holomorphic sectional curvature.

The classical theory of Riemann ellipsoids is formulated naturally as a gauge theory based on a principal G-bundle P{\cal P}. The structure group G=SO(3) is the vorticity group, and the bundle ${\cal P}=GL_+(3, R})$ is the connected component of the general linear group. The base manifold is the space of positive-defi…

1999-09-28abs ↗pdf ↗

Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of da…

2019-10-26abs ↗pdf ↗

New method efficiently learns positive-definite curvature for neural nets.

problem Efficiently learn positive-definite curvature for neural net training.
method Spectral-factorized positive-definite curvature learning approach.
result Efficiently applies arbitrary matrix roots and generic curvature learning.

Gaussian kernels on complex manifolds are never positive definite.

problem Analyzing positive definiteness of Gaussian kernels on non-simply-connected Riemannian manifolds.
method Combining recent preprint analysis and classical Riemannian geometry comparison theorems.
result Gaussian kernels are never positive definite on non-simply-connected closed Riemannian manifolds.