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The Riemannian metric on the manifold of positive definite matrices is defined by a kernel function $\phi$ in the form $K_D^\phi(H,K)=\sum_{i,j}\phi(\lambda_i,\lambda_j)^{-1} Tr P_iHP_jK$ when $\sum_i\lambda_iP_i$ is the spectral…

Mathematical Physics · Physics 2008-11-08 F. Hiai , D. Petz

This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive…

Optimization and Control · Mathematics 2009-10-21 Silvere Bonnabel , Rodolphe Sepulchre

Positive definite matrices abound in a dazzling variety of applications. This ubiquity can be in part attributed to their rich geometric structure: positive definite matrices form a self-dual convex cone whose strict interior is a…

Functional Analysis · Mathematics 2013-12-31 Suvrit Sra

If $A$ is a $2n \times 2n$ real positive definite matrix, then there exists a symplectic matrix $M$ such that $M^TAM = \left [ \begin{array}{cc} D & O \\ O & D \end{array} \right ]$ where $D= \diag (d_1 (A), \ldots, d_n(A))$ is a diagonal…

Mathematical Physics · Physics 2018-03-21 Rajendra Bhatia , Tanvi Jain

A Riemannian metric bundle G(M) is a fiber bundle over a smooth manifold M, whose fibers are the spaces of symmetric, positive-definite bilinear forms on the tangent spaces of M, which represent the Rieman?nian metrics. In this work, we aim…

Differential Geometry · Mathematics 2023-04-17 Shouvik Datta Choudhury

Recently, Riemannian Gaussian distributions were defined on spaces of positive-definite real and complex matrices. The present paper extends this definition to the space of positive-definite quaternion matrices. In order to do so, it…

Statistics Theory · Mathematics 2017-03-30 Salem Said , Nicolas Le Bihan , Jonathan H. Manton

In this paper, we generalize some matrix inequalities involving matrix power and Karcher means of positive definite matrices. Among other inequalities, it is shown that if ${\mathbb A}=(A_{1},...,A_{n})$ is a $n$-tuple of positive definite…

Functional Analysis · Mathematics 2017-02-27 Rahmatollah Lashkaripour , Monire Hajmohamadi , Mojtaba Bakherad

The real homology of a compact Riemannian manifold $M$ is naturally endowed with the stable norm. The stable norm on $H_1(M,\mathbb{R})$ arises from the Riemannian length functional by homogenization. It is difficult and interesting to…

Differential Geometry · Mathematics 2009-06-30 Madeleine Jotz

We show that, for each alpha in the interval (-1,1), the only Riemannian metrics on the space of positive definite matrices for which the alpha and -alpha-connections are mutually dual are matrix multiples fo the Wigner-Yanase-Dyson metric.…

Mathematical Physics · Physics 2007-05-23 M. R. Grasselli

We show how the Riemannian distance on $\mathbb{S}^n_{++}$, the cone of $n\times n$ real symmetric or complex Hermitian positive definite matrices, may be used to naturally define a distance between two such matrices of different…

Numerical Analysis · Mathematics 2018-06-06 Lek-Heng Lim , Rodolphe Sepulchre , Ke Ye

In this paper, we introduce properly-invariant diagonality measures of Hermitian positive-definite matrices. These diagonality measures are defined as distances or divergences between a given positive-definite matrix and its diagonal part.…

Information Theory · Computer Science 2016-09-06 Khaled Alyani , Marco Congedo , Maher Moakher

The Wasserstein distance on multivariate non-degenerate Gaussian densities is a Riemannian distance. After reviewing the properties of the distance and the metric geodesic, we present an explicit form of the Riemannian metrics on…

Statistics Theory · Mathematics 2018-09-25 Luigi Malagò , Luigi Montrucchio , Giovanni Pistone

In this paper, we define the geometric median of a probability measure on a Riemannian manifold, give its characterization and a natural condition to ensure its uniqueness. In order to calculate the median in practical cases, we also…

Differential Geometry · Mathematics 2019-02-20 Le Yang

We study the stability of the Positive Mass Theorem using the Intrinsic Flat Distance. In particular we consider the class of complete asymptotically flat rotationally symmetric Riemannian manifolds with nonnegative scalar curvature and no…

Differential Geometry · Mathematics 2015-03-19 Dan A. Lee , Christina Sormani

There is a sequence of positive numbers $\delta_{2n}$, such that for any connected $2n$-dimensional Riemannian manifold $M$, there are two mutually exclusive possibilities: $1)$ There is a complex structure on $M$ making it into a K\"ahler…

Differential Geometry · Mathematics 2018-02-20 Scott O. Wilson , Mahmoud Zeinalian

In this work, we generalize the probability simplex constraint to matrices, i.e., $\mathbf{X}_1 + \mathbf{X}_2 + \ldots + \mathbf{X}_K = \mathbf{I}$, where $\mathbf{X}_i \succeq 0$ is a symmetric positive semidefinite matrix of size…

Optimization and Control · Mathematics 2020-11-18 Bamdev Mishra , Hiroyuki Kasai , Pratik Jawanpuria

We prove that if R is a principal ideal ring and A\in\M_n(R) is a matrix with trace zero, then A is a commutator, that is, A=XY-YX for some X,Y\in\M_n(R). This generalises the corresponding result over fields due to Albert and Muckenhoupt,…

Rings and Algebras · Mathematics 2013-02-26 Alexander Stasinski

Consider the sum of the first $N$ eigenspaces for the Laplacian on a Riemannian manifold. A basis for this space determines a map to Euclidean space and for $N$ sufficiently large the map is an embedding. In analogy with a fruitful idea of…

Differential Geometry · Mathematics 2014-04-30 Eric Potash

In the setting of finite-dimensional $C^*$-algebras ${\mathcal A}$ we define what we call a Riemannian metric for ${\mathcal A}$, which when ${\mathcal A}$ is commutative is very closely related to a finite resistance network. We explore…

Operator Algebras · Mathematics 2014-06-17 Marc A. Rieffel

Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties…

Machine Learning · Statistics 2024-03-19 Wu Lin , Valentin Duruisseaux , Melvin Leok , Frank Nielsen , Mohammad Emtiyaz Khan , Mark Schmidt
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