We propose two numerical methods, namely the alternating block relaxation method and the alternating majorization method, for the problem of nearest correlation matrix with factor structure, which is highly nonconvex. In the block relaxation method, the subproblem is of the standard trust region problem, which is solved by Steighaug’s truncated conjugate gradient method or by the exact trust region method. In the majorization method, the subproblem has a closed-form solution. We then apply the majorization method to the case where nonnegative factors are required. The numerical results confirm that the proposed methods work quite well and are competitive against the best available methods. <br/
Abstract The standard nearest correlation matrix can be efficiently computed by ex-ploiting a recent...
AbstractWe desire to find a correlation matrix R^ of a given rank that is as close as possible to an...
We present a dedicated algorithm for the nonnegative factorization of a correlation matrix from an a...
Block relaxation methods, Majorization methods, Correlation matrix, Factor structure,
textabstractIn this paper a novel method is developed for the problem of finding a low-rank correlat...
An $n\times n$ correlation matrix has $k$ factor structure if its off-diagonal agrees with that of a...
An $n\times n$ correlation matrix has $k$ factor structure if its off-diagonal agrees with that of a...
A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a...
An $n\times n$ correlation matrix has $k$ factor structure if its off-diagonal agrees with that of a...
The standard nearest correlation matrix can be efficiently computed by exploiting a recent developme...
Abstract. A novel algorithm is developed for the problem of finding a low-rank correlation matrix ne...
Correlation matrices have many applications, particularly in marketing and financial economics - suc...
For n-dimensional real-valued matrix A, the computation of nearest correlation matrix; that is, a sy...
Firstly, we describe and investigate the algorithm of Qi and Sun which solves the problem of finding...
AbstractWe present a dedicated algorithm for the nonnegative factorization of a correlation matrix f...
Abstract The standard nearest correlation matrix can be efficiently computed by ex-ploiting a recent...
AbstractWe desire to find a correlation matrix R^ of a given rank that is as close as possible to an...
We present a dedicated algorithm for the nonnegative factorization of a correlation matrix from an a...
Block relaxation methods, Majorization methods, Correlation matrix, Factor structure,
textabstractIn this paper a novel method is developed for the problem of finding a low-rank correlat...
An $n\times n$ correlation matrix has $k$ factor structure if its off-diagonal agrees with that of a...
An $n\times n$ correlation matrix has $k$ factor structure if its off-diagonal agrees with that of a...
A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a...
An $n\times n$ correlation matrix has $k$ factor structure if its off-diagonal agrees with that of a...
The standard nearest correlation matrix can be efficiently computed by exploiting a recent developme...
Abstract. A novel algorithm is developed for the problem of finding a low-rank correlation matrix ne...
Correlation matrices have many applications, particularly in marketing and financial economics - suc...
For n-dimensional real-valued matrix A, the computation of nearest correlation matrix; that is, a sy...
Firstly, we describe and investigate the algorithm of Qi and Sun which solves the problem of finding...
AbstractWe present a dedicated algorithm for the nonnegative factorization of a correlation matrix f...
Abstract The standard nearest correlation matrix can be efficiently computed by ex-ploiting a recent...
AbstractWe desire to find a correlation matrix R^ of a given rank that is as close as possible to an...
We present a dedicated algorithm for the nonnegative factorization of a correlation matrix from an a...