Auxiliary for use as a delegate parameter
Syntax
C# |
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public static void e04fdz( int m, int n, double[] xc, double[] fvec, double[,] fjac, double[] s, int igrade, int niter, int nf ) |
Visual Basic |
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Public Shared Sub e04fdz ( _ m As Integer, _ n As Integer, _ xc As Double(), _ fvec As Double(), _ fjac As Double(,), _ s As Double(), _ igrade As Integer, _ niter As Integer, _ nf As Integer _ ) |
Visual C++ |
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public: static void e04fdz( int m, int n, array<double>^ xc, array<double>^ fvec, array<double,2>^ fjac, array<double>^ s, int igrade, int niter, int nf ) |
F# |
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static member e04fdz : m : int * n : int * xc : float[] * fvec : float[] * fjac : float[,] * s : float[] * igrade : int * niter : int * nf : int -> unit |
Parameters
- m
- Type: System..::..Int32On entry: the numbers and of residuals and variables, respectively.
- n
- Type: System..::..Int32On entry: the numbers and of residuals and variables, respectively.
- xc
- Type: array<System..::..Double>[]()[][]An array of size [n]On entry: the coordinates of the current point .
- fvec
- Type: array<System..::..Double>[]()[][]An array of size [m]On entry: the values of the residuals at the current point .
- fjac
- Type: array<System..::..Double,2>[,](,)[,][,]On entry: contains the value of at the current point , for and .
- s
- Type: array<System..::..Double>[]()[][]An array of size [n]On entry: the singular values of the current approximation to the Jacobian matrix. Thus s may be useful as information about the structure of your problem.
- igrade
- Type: System..::..Int32On entry: e04fd estimates the dimension of the subspace for which the Jacobian matrix can be used as a valid approximation to the curvature (see Gill and Murray (1978)). This estimate is called the grade of the Jacobian matrix, and igrade gives its current value.
- niter
- Type: System..::..Int32On entry: the number of iterations which have been performed in e04fd.
- nf
- Type: System..::..Int32On entry: the number of times that _lsqfun has been called so far. (However, for intermediate calls of _lsqmon, nf is calculated on the assumption that the latest linear search has been successful. If this is not the case, then the evaluations allowed for approximating the Jacobian at the new point will not in fact have been made. nf will be accurate at the final call of _lsqmon.)
Description
Auxiliary for use as a delegate parameter
References
Gill P E and Murray W (1978) Algorithms for the solution of the nonlinear least squares problem SIAM J. Numer. Anal. 15 977–992
Error Indicators and Warnings
None.
Accuracy
Not applicable.
Further Comments
None.
Example
None.