NAG Library Routine Document
E04HYF
1 Purpose
E04HYF is an easytouse modified Gauss–Newton algorithm for finding an unconstrained minimum of a sum of squares of $m$ nonlinear functions in $n$ variables $\left(m\ge n\right)$. First and second 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
SUBROUTINE E04HYF ( 
M, N, LSFUN2, LSHES2, X, FSUMSQ, W, LW, IUSER, RUSER, IFAIL) 
INTEGER 
M, N, LW, IUSER(*), IFAIL 
REAL (KIND=nag_wp) 
X(N), FSUMSQ, W(LW), RUSER(*) 
EXTERNAL 
LSFUN2, LSHES2 

3 Description
E04HYF is similar to the subroutine LSSDN2 in the NPL Algorithms Library. It is applicable to problems of the form:
where
$x={\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)}^{\mathrm{T}}$ and
$m\ge n$. (The functions
${f}_{i}\left(x\right)$ are often referred to as ‘residuals’.)
You must supply a subroutine to evaluate the residuals and their first derivatives at any point $x$, and a subroutine to evaluate the elements of the second derivative term of the Hessian matrix of $F\left(x\right)$.
Before attempting to minimize the sum of squares, the algorithm checks the usersupplied subroutines 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 $F\left(x\right)$.
4 References
Gill P E and Murray W (1978) Algorithms for the solution of the nonlinear leastsquares problem SIAM J. Numer. Anal. 15 977–992
5 Parameters
 1: M – INTEGERInput
 2: N – INTEGERInput
On entry: the number $m$ of residuals, ${f}_{i}\left(x\right)$, and the number $n$ of variables, ${x}_{j}$.
Constraint:
$1\le {\mathbf{N}}\le {\mathbf{M}}$.
 3: LSFUN2 – SUBROUTINE, supplied by the user.External Procedure
You must supply this routine to calculate the vector of values
${f}_{i}\left(x\right)$ and the Jacobian matrix of first derivatives
$\frac{\partial {f}_{i}}{\partial {x}_{j}}$ at any point
$x$. It should be tested separately before being used in conjunction with E04HYF (see the
E04 Chapter Introduction).
The specification of
LSFUN2 is:
INTEGER 
M, N, LDFJAC, IUSER(*) 
REAL (KIND=nag_wp) 
XC(N), FVEC(M), FJAC(LDFJAC,N), RUSER(*) 

Important: the dimension declaration for
FJAC must contain the variable
LDFJAC, not an integer constant.
 1: M – INTEGERInput
On entry: $m$, the numbers of residuals.
 2: N – INTEGERInput
On entry: $n$, the numbers of variables.
 3: XC(N) – REAL (KIND=nag_wp) arrayInput
On entry: the point $x$ at which the values of the ${f}_{i}$ and the $\frac{\partial {f}_{i}}{\partial {x}_{j}}$ are required.
 4: FVEC(M) – REAL (KIND=nag_wp) arrayOutput
On exit: ${\mathbf{FVEC}}\left(i\right)$ must be set to the value of
${f}_{\mathit{i}}$ at the point $x$, for $\mathit{i}=1,2,\dots ,m$.
 5: FJAC(LDFJAC,N) – REAL (KIND=nag_wp) arrayOutput
On exit: ${\mathbf{FJAC}}\left(\mathit{i},\mathit{j}\right)$ must be set to the value of $\frac{\partial {f}_{\mathit{i}}}{\partial {x}_{\mathit{j}}}$ at the point $x$, for $\mathit{i}=1,2,\dots ,m$ and $\mathit{j}=1,2,\dots ,n$.
 6: LDFJAC – INTEGERInput
On entry: the first dimension of the array
FJAC as declared in the (sub)program from which E04HYF is called.
 7: IUSER($*$) – INTEGER arrayUser Workspace
 8: RUSER($*$) – REAL (KIND=nag_wp) arrayUser Workspace

LSFUN2 is called with the parameters
IUSER and
RUSER as supplied to E04HYF. You are free to use the arrays
IUSER and
RUSER to supply information to
LSFUN2 as an alternative to using COMMON global variables.
LSFUN2 must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which E04HYF is called. Parameters denoted as
Input must
not be changed by this procedure.
 4: LSHES2 – SUBROUTINE, supplied by the user.External Procedure
You must supply this routine to calculate the elements of the symmetric matrix
at any point
$x$, where
${G}_{i}\left(x\right)$ is the Hessian matrix of
${f}_{i}\left(x\right)$. It should be tested separately before being used in conjunction with E04HYF (see the
E04 Chapter Introduction).
The specification of
LSHES2 is:
INTEGER 
M, N, LB, IUSER(*) 
REAL (KIND=nag_wp) 
FVEC(M), XC(N), B(LB), RUSER(*) 

 1: M – INTEGERInput
On entry: $m$, the number of residuals.
 2: N – INTEGERInput
On entry: $n$, the number of residuals.
 3: FVEC(M) – REAL (KIND=nag_wp) arrayInput
On entry: the value of the residual
${f}_{\mathit{i}}$ at the point
$x$, for
$\mathit{i}=1,2,\dots ,m$, so that the values of the
${f}_{\mathit{i}}$ can be used in the calculation of the elements of
B.
 4: XC(N) – REAL (KIND=nag_wp) arrayInput
On entry: the point
$x$ at which the elements of
B are to be evaluated.
 5: B(LB) – REAL (KIND=nag_wp) arrayOutput
On exit: must contain the lower triangle of the matrix $B\left(x\right)$, evaluated at the point $x$, stored by rows. (The upper triangle is not required because the matrix is symmetric.) More precisely,
${\mathbf{B}}\left(\mathit{j}\left(\mathit{j}1\right)/2+\mathit{k}\right)$ must contain $\sum _{\mathit{i}=1}^{m}}{f}_{\mathit{i}}\frac{{\partial}^{2}{f}_{\mathit{i}}}{\partial {x}_{\mathit{j}}\partial {x}_{\mathit{k}}$ evaluated at the point $x$, for $\mathit{j}=1,2,\dots ,n$ and $\mathit{k}=1,2,\dots ,\mathit{j}$.
 6: LB – INTEGERInput
On entry: the length of the array
B.
 7: IUSER($*$) – INTEGER arrayUser Workspace
 8: RUSER($*$) – REAL (KIND=nag_wp) arrayUser Workspace

LSHES2 is called with the parameters
IUSER and
RUSER as supplied to E04HYF. You are free to use the arrays
IUSER and
RUSER to supply information to
LSHES2 as an alternative to using COMMON global variables.
LSHES2 must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which E04HYF is called. Parameters denoted as
Input must
not be changed by this procedure.
 5: X(N) – REAL (KIND=nag_wp) arrayInput/Output
On entry:
${\mathbf{X}}\left(\mathit{j}\right)$ must be set to a guess at the
$\mathit{j}$th component of the position of the minimum, for
$\mathit{j}=1,2,\dots ,n$. The routine checks
LSFUN2 and
LSHES2 at the starting point and so is more likely to detect any error in your routines if the initial
${\mathbf{X}}\left(j\right)$ are nonzero and mutually distinct.
On exit: the lowest point found during the calculations. Thus, if ${\mathbf{IFAIL}}={\mathbf{0}}$ on exit, ${\mathbf{X}}\left(j\right)$ is the $j$th component of the position of the minimum.
 6: FSUMSQ – REAL (KIND=nag_wp)Output
On exit: the value of the sum of squares,
$F\left(x\right)$, corresponding to the final point stored in
X.
 7: W(LW) – REAL (KIND=nag_wp) arrayWorkspace
 8: LW – INTEGERInput
On entry: the dimension of the array
W as declared in the (sub)program from which E04HYF is called.
Constraints:
 if ${\mathbf{N}}>1$, ${\mathbf{LW}}\ge 8\times {\mathbf{N}}+2\times {\mathbf{N}}\times {\mathbf{N}}+2\times {\mathbf{M}}\times {\mathbf{N}}+3\times {\mathbf{M}}$;
 if ${\mathbf{N}}=1$, ${\mathbf{LW}}\ge 11+5\times {\mathbf{M}}$.
 9: IUSER($*$) – INTEGER arrayUser Workspace
 10: RUSER($*$) – REAL (KIND=nag_wp) arrayUser Workspace

IUSER and
RUSER are not used by E04HYF, but are passed directly to
LSFUN2 and
LSHES2 and may be used to pass information to these routines as an alternative to using COMMON global variables.
 11: IFAIL – INTEGERInput/Output

On entry:
IFAIL must be set to
$0$,
$1\text{ or}1$. If you are unfamiliar with this parameter you should refer to
Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{ or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, because for this routine the values of the output parameters may be useful even if
${\mathbf{IFAIL}}\ne {\mathbf{0}}$ on exit, the recommended value is
$1$.
When the value $\mathbf{1}\text{ or}1$ is used it is essential to test the value of IFAIL on exit.
On exit:
${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
${\mathbf{IFAIL}}={\mathbf{0}}$ or
${{\mathbf{1}}}$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Note: E04HYF may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$

On entry,  ${\mathbf{N}}<1$, 
or  ${\mathbf{M}}<{\mathbf{N}}$, 
or  ${\mathbf{LW}}<8\times {\mathbf{N}}+2\times {\mathbf{N}}\times {\mathbf{N}}+2\times {\mathbf{M}}\times {\mathbf{N}}+3\times {\mathbf{M}}$, when ${\mathbf{N}}>1$, 
or  ${\mathbf{LW}}<11+5\times {\mathbf{M}}$, when ${\mathbf{N}}=1$. 
 ${\mathbf{IFAIL}}=2$
There have been
$50\times n$ calls of
LSFUN2, yet 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 E04HYF from the final point held in
X.
 ${\mathbf{IFAIL}}=3$
The final point does not satisfy the conditions for acceptance as a minimum, but no lower point could be found.
 ${\mathbf{IFAIL}}=4$
An auxiliary routine has been unable to complete a singular value decomposition in a reasonable number of subiterations.
 ${\mathbf{IFAIL}}=5$
 ${\mathbf{IFAIL}}=6$
 ${\mathbf{IFAIL}}=7$
 ${\mathbf{IFAIL}}=8$
There is some doubt about whether the point
$x$ found by E04HYF is a minimum of
$F\left(x\right)$. The degree of confidence in the result decreases as
IFAIL increases. Thus, when
${\mathbf{IFAIL}}={\mathbf{5}}$ it is probable that the final
$x$ gives a good estimate of the position of a minimum, but when
${\mathbf{IFAIL}}={\mathbf{8}}$ it is very unlikely that the routine has found a minimum.
 ${\mathbf{IFAIL}}=9$
It is very likely that you have made an error in forming the derivatives
$\frac{\partial {f}_{i}}{\partial {x}_{j}}$ in
LSFUN2.
 ${\mathbf{IFAIL}}=10$
It is very likely that you have made an error in forming the quantities
${B}_{jk}$ in
LSHES2.
If you are not satisfied with the result (e.g., because
IFAIL lies between
$3$ and
$8$), 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 $t$ decimals, one would expect to get about $t/21$ decimals accuracy in the components of $x$ and between $t1$ (if $F\left(x\right)$ is of order $1$ at the minimum) and $2t2$ (if $F\left(x\right)$ is close to zero at the minimum) decimals accuracy in $F\left(x\right)$.
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 E04HYF varies, but for
$m\gg n$ is approximately
$n\times {m}^{2}+\mathit{O}\left({n}^{3}\right)$. In addition, each iteration makes at least one call of
LSFUN2 and some iterations may call
LSHES2. So, unless the residuals and their derivatives can be evaluated very quickly, the run time will be dominated by the time spent in
LSFUN2 (and, to a lesser extent, in
LSHES2).
Ideally, the problem should be scaled so that the minimum value of the sum of squares is in the range $\left(0,+1\right)$ 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 E04HYF will take less computer time.
When the sum of squares represents the goodnessoffit of a nonlinear model to observed data, elements of the variancecovariance 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.
9 Example
This example finds least squares estimates of
${x}_{1}$,
${x}_{2}$ and
${x}_{3}$ in the model
using the
$15$ sets of data given in the following table.
The program uses
$\left(0.5,1.0,1.5\right)$ as the initial guess at the position of the minimum.
9.1 Program Text
Program Text (e04hyfe.f90)
9.2 Program Data
Program Data (e04hyfe.d)
9.3 Program Results
Program Results (e04hyfe.r)