Add to ORKGA majorization-minimization (MM) algorithm for the Karcher mean of n p × p positive definite matrices is proposed and it is gauranteed to converge linearly. Simulations show that the MM algorithm performs faster than other current algorithms for the Karcher mean of positive definite matrices, including steepest descent, conjugate gradient descent and trust region methods.
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a...
We explore the connection between two problems that have arisen independently in the signal processi...
An algorithm for computing the Karcher mean of n positive definite matrices is proposed, based on th...
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...
AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positi...
In this paper we present a survey of various algorithms for computing matrix geometric means and der...
In this paper, we present a survey of various algorithms for computing matrix geometric means and de...
AbstractVarious optimization algorithms have been proposed to compute the Karcher mean (namely the R...
Positive definite matrices can be encountered in a widespread collection of applications, such as si...
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positi...
We propose a conjugate gradient type optimization technique for the computa-tion of the Karcher mean...
When computing an average of positive definite (PD) matrices, the preservation of additional matrix ...
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a...
We explore the connection between two problems that have arisen independently in the signal processi...
An algorithm for computing the Karcher mean of n positive definite matrices is proposed, based on th...
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...
AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positi...
In this paper we present a survey of various algorithms for computing matrix geometric means and der...
In this paper, we present a survey of various algorithms for computing matrix geometric means and de...
AbstractVarious optimization algorithms have been proposed to compute the Karcher mean (namely the R...
Positive definite matrices can be encountered in a widespread collection of applications, such as si...
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
AbstractWe define a new family of matrix means {Pt(ω;A)}t∈[−1,1], where ω and A vary over all positi...
We propose a conjugate gradient type optimization technique for the computa-tion of the Karcher mean...
When computing an average of positive definite (PD) matrices, the preservation of additional matrix ...
The geometric mean of positive definite matrices is usually identified with the Karcher mean, which ...
A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a...
We explore the connection between two problems that have arisen independently in the signal processi...