AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positive probability vectors in Rn and n-tuples of positive definite matrices resp. Each of these means except t≠0 arises as a unique positive definite solution of a non-linear matrix equation, satisfies all desirable properties of power means of positive real numbers and interpolates between the weighted harmonic and arithmetic means. The main result is that the Karcher mean coincides with the limit of power means as t→0. This provides not only a sequence of matrix means converging to the Karcher mean, but also a simple proof of the monotonicity of the Karcher mean, conjectured by Bhatia and Holbrook, and other new properties, which have recently ...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positi...
We show that, the least squares mean on the Riemannian manifold σ of positive operators in the exten...
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
In this talk we will study the different ways the power means of positive numbers can be extended to...
AbstractWe define a new family of matrix means {Lμ(ω;A)}μ∈R where ω and A vary over all positive pro...
We define a new family of matrix means {Lμ(ω;A)} μ∈R where ω and A vary over all positive probabilit...
We propose a new algorithm to approximate the Karcher mean of N symmetric positive definite (SDP) ma...
AbstractVarious optimization algorithms have been proposed to compute the Karcher mean (namely the R...
In this article we introduce a new family of power means for m positive definite matrices, called Ré...
Positive definite matrices can be encountered in a widespread collection of applications, such as si...
A majorization-minimization (MM) algorithm for the Karcher mean of n p × p positive definite matrice...
The main concern of this paper is the three-variable Karcher (alternatively, Riemannian or Cartan) m...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positi...
We show that, the least squares mean on the Riemannian manifold σ of positive operators in the exten...
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
In this talk we will study the different ways the power means of positive numbers can be extended to...
AbstractWe define a new family of matrix means {Lμ(ω;A)}μ∈R where ω and A vary over all positive pro...
We define a new family of matrix means {Lμ(ω;A)} μ∈R where ω and A vary over all positive probabilit...
We propose a new algorithm to approximate the Karcher mean of N symmetric positive definite (SDP) ma...
AbstractVarious optimization algorithms have been proposed to compute the Karcher mean (namely the R...
In this article we introduce a new family of power means for m positive definite matrices, called Ré...
Positive definite matrices can be encountered in a widespread collection of applications, such as si...
A majorization-minimization (MM) algorithm for the Karcher mean of n p × p positive definite matrice...
The main concern of this paper is the three-variable Karcher (alternatively, Riemannian or Cartan) m...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD...