NAG FL Interface
e04gzf (lsq_uncon_mod_deriv_easy)
1
Purpose
e04gzf is an easy-to-use modified Gauss–Newton algorithm for finding an unconstrained minimum of a sum of squares of nonlinear functions in variables . First derivatives are required.
It is intended for functions which are continuous and which have continuous first and second derivatives (although it will usually work even if the derivatives have occasional discontinuities).
2
Specification
Fortran Interface
Integer, Intent (In) |
:: |
m, n, lw |
Integer, Intent (Inout) |
:: |
iuser(*), ifail |
Real (Kind=nag_wp), Intent (Inout) |
:: |
x(n), ruser(*) |
Real (Kind=nag_wp), Intent (Out) |
:: |
fsumsq, w(lw) |
External |
:: |
lsfun2 |
|
C Header Interface
#include <nag.h>
void |
e04gzf_ (const Integer *m, const Integer *n, void (NAG_CALL *lsfun2)(const Integer *m, const Integer *n, const double xc[], double fvec[], double fjac[], const Integer *ldfjac, Integer iuser[], double ruser[]), double x[], double *fsumsq, double w[], const Integer *lw, Integer iuser[], double ruser[], Integer *ifail) |
|
C++ Header Interface
#include <nag.h> extern "C" {
void |
e04gzf_ (const Integer &m, const Integer &n, void (NAG_CALL *lsfun2)(const Integer &m, const Integer &n, const double xc[], double fvec[], double fjac[], const Integer &ldfjac, Integer iuser[], double ruser[]), double x[], double &fsumsq, double w[], const Integer &lw, Integer iuser[], double ruser[], Integer &ifail) |
}
|
The routine may be called by the names e04gzf or nagf_opt_lsq_uncon_mod_deriv_easy.
3
Description
e04gzf is similar to the subroutine LSFDN2 in the NPL Algorithms Library. It is applicable to problems of the form
where
and
. (The functions
are often referred to as ‘residuals’.)
You must supply a subroutine to evaluate the residuals and their first derivatives at any point .
Before attempting to minimize the sum of squares, the algorithm checks the subroutine for consistency. Then, from a starting point supplied by you, a sequence of points is generated which is intended to converge to a local minimum of the sum of squares. These points are generated using estimates of the curvature of .
4
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
5
Arguments
-
1:
– Integer
Input
-
2:
– Integer
Input
-
On entry: the number of residuals, , and the number of variables, .
Constraint:
.
-
3:
– Subroutine, supplied by the user.
External Procedure
-
You must supply this routine to calculate the vector of values and the Jacobian matrix of first derivatives at any point . It should be tested separately before being used in conjunction with e04gzf.
The specification of
lsfun2 is:
Fortran Interface
Integer, Intent (In) |
:: |
m, n, ldfjac |
Integer, Intent (Inout) |
:: |
iuser(*) |
Real (Kind=nag_wp), Intent (In) |
:: |
xc(n) |
Real (Kind=nag_wp), Intent (Inout) |
:: |
fjac(ldfjac,n), ruser(*) |
Real (Kind=nag_wp), Intent (Out) |
:: |
fvec(m) |
|
C Header Interface
void |
lsfun2_ (const Integer *m, const Integer *n, const double xc[], double fvec[], double fjac[], const Integer *ldfjac, Integer iuser[], double ruser[]) |
|
C++ Header Interface
#include <nag.h> extern "C" {
void |
lsfun2_ (const Integer &m, const Integer &n, const double xc[], double fvec[], double fjac[], const Integer &ldfjac, Integer iuser[], double ruser[]) |
}
|
Important: the dimension declaration for
fjac must contain the variable
ldfjac, not an integer constant.
-
1:
– Integer
Input
-
On entry: , the numbers of residuals.
-
2:
– Integer
Input
-
On entry: , the numbers of variables.
-
3:
– Real (Kind=nag_wp) array
Input
-
On entry: the point at which the values of the and the are required.
-
4:
– Real (Kind=nag_wp) array
Output
-
On exit: must be set to the value of
at the point , for .
-
5:
– Real (Kind=nag_wp) array
Output
-
On exit: must be set to the value of at the point , for and .
-
6:
– Integer
Input
-
On entry: the first dimension of the array
fjac, set to
by
e04gzf.
-
7:
– Integer array
User Workspace
-
8:
– Real (Kind=nag_wp) array
User Workspace
-
lsfun2 is called with the arguments
iuser and
ruser as supplied to
e04gzf. You should use the arrays
iuser and
ruser to supply information to
lsfun2.
lsfun2 must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which
e04gzf is called. Arguments denoted as
Input must
not be changed by this procedure.
Note: lsfun2 should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by
e04gzf. If your code inadvertently
does return any NaNs or infinities,
e04gzf is likely to produce unexpected results.
-
4:
– Real (Kind=nag_wp) array
Input/Output
-
On entry:
must be set to a guess at the
th component of the position of the minimum, for
. The routine checks the first derivatives calculated by
lsfun2 at the starting point and so is more likely to detect any error in your routines if the initial
are nonzero and mutually distinct.
On exit: the lowest point found during the calculations. Thus, if on exit, is the th component of the position of the minimum.
-
5:
– Real (Kind=nag_wp)
Output
-
On exit: the value of the sum of squares,
, corresponding to the final point stored in
x.
-
6:
– Real (Kind=nag_wp) array
Communication Array
-
7:
– Integer
Input
-
On entry: the dimension of the array
w as declared in the (sub)program from which
e04gzf is called.
Constraints:
- if , ;
- if , .
-
8:
– Integer array
User Workspace
-
9:
– Real (Kind=nag_wp) array
User Workspace
-
iuser and
ruser are not used by
e04gzf, but are passed directly to
lsfun2 and may be used to pass information to this routine.
-
10:
– Integer
Input/Output
-
On entry:
ifail must be set to
,
. If you are unfamiliar with this argument you should refer to
Section 4 in the Introduction to the NAG Library FL Interface for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, because for this routine the values of the output arguments may be useful even if
on exit, the recommended value is
.
When the value is used it is essential to test the value of ifail on exit.
On exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
6
Error Indicators and Warnings
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Errors or warnings detected by the routine:
Note: in some cases e04gzf may return useful information.
-
On entry, and .
Constraint: .
On entry, .
Constraint: .
On entry, and .
Constraint: if then ; that is, .
On entry, and .
Constraint: if then ; that is, .
-
There have been
calls to
lsfun2.
The algorithm does not seem to have converged. This may be due to an awkward function or to a poor starting point, so it is worth restarting
e04gzf from the final point held in
x.
-
The conditions for a minimum have not all been satisfied, but a lower point could not be found. See
Section 7 for further information.
-
Failure in computing SVD of Jacobian matrix.
-
It is probable that a local minimum has been found, but it cannot be guaranteed.
-
It is possible that a local minimum has been found, but it cannot be guaranteed.
-
It is unlikely that a local minimum has been found.
-
It is very unlikely that a local minimum has been found.
-
It is very likely that you have made an error in forming the derivatives in
lsfun2.
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 7 in the Introduction to the NAG Library FL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library FL Interface for further information.
Dynamic memory allocation failed.
See
Section 9 in the Introduction to the NAG Library FL Interface for further information.
If you are not satisfied with the result (e.g., because
ifail lies between
and
), it is worth restarting the calculations from a different starting point (not the point at which the failure occurred) in order to avoid the region which caused the failure. Repeated failure may indicate some defect in the formulation of the problem.
7
Accuracy
If the problem is reasonably well scaled and a successful exit is made, then, for a computer with a mantissa of decimals, one would expect to get about decimals accuracy in the components of and between (if is of order at the minimum) and (if is close to zero at the minimum) decimals accuracy in .
8
Parallelism and Performance
e04gzf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
e04gzf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the
X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the
Users' Note for your implementation for any additional implementation-specific information.
The number of iterations required depends on the number of variables, the number of residuals and their behaviour, and the distance of the starting point from the solution. The number of multiplications performed per iteration of
e04gzf varies, but for
is approximately
. In addition, each iteration makes at least one call of
lsfun2. So, unless the residuals and their derivatives can be evaluated very quickly, the run time will be dominated by the time spent in
lsfun2.
Ideally, the problem should be scaled so that the minimum value of the sum of squares is in the range and so that at points a unit distance away from the solution the sum of squares is approximately a unit value greater than at the minimum. It is unlikely that you will be able to follow these recommendations very closely, but it is worth trying (by guesswork), as sensible scaling will reduce the difficulty of the minimization problem, so that e04gzf will take less computer time.
When the sum of squares represents the goodness-of-fit of a nonlinear model to observed data, elements of the variance-covariance matrix of the estimated regression coefficients can be computed by a subsequent call to
e04ycf, using information returned in segments of the workspace array
w. See
e04ycf for further details.
10
Example
This example finds least squares estimates of
,
and
in the model
using the
sets of data given in the following table.
The program uses
as the initial guess at the position of the minimum.
10.1
Program Text
10.2
Program Data
10.3
Program Results