A necessary condition for a real valued Frechet differentiable function of a vector variable have an extremum at a vector x sub 0 is that the Frechet derivative vanishes at x sub 0. A relationship between Frechet differentials and matrix derivatives was established that obtains a necessary condition on the matrix derivative at an extrema. These results are applied to various scalar functions of matrix variables which occur in statistical pattern recognition
We design a block Krylov method to compute the action of the Fréchet derivative of a matrix function...
The Fr\'echet derivative $L_f$ of a matrix function $f \colon \mathbb{C}^{n\times n} \mapsto \mathbb...
This paper collects together a number of matrix derivative results which are very useful in forward ...
AbstractThis paper explores the connections between symbolic matrix derivatives as developed in the ...
AbstractThe behavior of special differentiable real-valued functions defined on a set of matrices is...
AbstractThis paper explores the connections between symbolic matrix derivatives as developed in the ...
AbstractIn 1956 Rinehart [4] discussed the derivatives of matrix functions by considering difference...
AbstractWe review and extend some recent results concerning the structure of pattern-reduction matri...
This paper collects together a number of matrix derivative results which are very useful in forward ...
Partial least squares is a common technique for multivariate regression. The pro- cedure is recursiv...
Partial least squares is a common technique for multivariate regression. The pro- cedure is recursiv...
Often in engineering, the design requirements are to find a complex-valued matrix which minimizes or...
We design a block Krylov method to compute the action of the Frechet derivative of a matrix function...
The Fr\'echet derivative $L_f$ of a matrix function $f \colon \mathbb{C}^{n\times n} \mapsto \mathbb...
This paper collects together a number of matrix derivative results which are very useful in forward ...
We design a block Krylov method to compute the action of the Fréchet derivative of a matrix function...
The Fr\'echet derivative $L_f$ of a matrix function $f \colon \mathbb{C}^{n\times n} \mapsto \mathbb...
This paper collects together a number of matrix derivative results which are very useful in forward ...
AbstractThis paper explores the connections between symbolic matrix derivatives as developed in the ...
AbstractThe behavior of special differentiable real-valued functions defined on a set of matrices is...
AbstractThis paper explores the connections between symbolic matrix derivatives as developed in the ...
AbstractIn 1956 Rinehart [4] discussed the derivatives of matrix functions by considering difference...
AbstractWe review and extend some recent results concerning the structure of pattern-reduction matri...
This paper collects together a number of matrix derivative results which are very useful in forward ...
Partial least squares is a common technique for multivariate regression. The pro- cedure is recursiv...
Partial least squares is a common technique for multivariate regression. The pro- cedure is recursiv...
Often in engineering, the design requirements are to find a complex-valued matrix which minimizes or...
We design a block Krylov method to compute the action of the Frechet derivative of a matrix function...
The Fr\'echet derivative $L_f$ of a matrix function $f \colon \mathbb{C}^{n\times n} \mapsto \mathbb...
This paper collects together a number of matrix derivative results which are very useful in forward ...
We design a block Krylov method to compute the action of the Fréchet derivative of a matrix function...
The Fr\'echet derivative $L_f$ of a matrix function $f \colon \mathbb{C}^{n\times n} \mapsto \mathbb...
This paper collects together a number of matrix derivative results which are very useful in forward ...