NAG Library Function Document

nag_opt_estimate_deriv (e04xac)

nag_opt_estimate_deriv (e04xac) computes an approximation to the gradient vector and/or the Hessian matrix for use in conjunction with, or following the use of an optimization function (such as nag_opt_nlp (e04ucc)).

2 Specification

#include <nag.h>

#include <nage04.h>

void

nag_opt_estimate_deriv (Integer n, double x[],

void	(objfun)(Integer n, const double x[], double objf, double g[], Nag_Comm *comm),

double *objf, double g[], double h_forward[], double h_central[], double hess[], Integer tdhess, Nag_DerivInfo *deriv_info, Nag_E04_Opt *options, Nag_Comm *comm, NagError *fail)

3 Description

nag_opt_estimate_deriv (e04xac) is based on the routine FDCALC described in Gill et al. (1983a). It computes finite difference approximations to the gradient vector and the Hessian matrix for a given function, and aims to provide sufficiently accurate estimates for use with an optimization algorithm.

The simplest approximation of the gradients involves the forward-difference formula, in which the derivative of

f^{'} (x)

of a univariate function

f (x)

is approximated by the quantity

ρ_{F} (f, h) = \frac{f (x + h) - f (x)}{h}

for some interval

h > 0

, where the subscript ‘F’ denotes ‘forward-difference’ (see Gill et al. (1983b)).

The choice of which gradients are returned by nag_opt_estimate_deriv (e04xac) is controlled by the optional argument

options . deriv_want

(see Section 11 for a description of this argument). To summarise the procedure used by nag_opt_estimate_deriv (e04xac) when

options . deriv_want = Nag_Grad_HessFull

(default value) (i.e., for the case when the objective function is available and you require estimates of gradient values and the full Hessian matrix) consider a univariate function

f

at the point

x

. (In order to obtain the gradient of a multivariate function

F (x)

, where

x

is an

n

-vector, the procedure is applied to each component of

x

, keeping the other components fixed.) Roughly speaking, the method is based on the fact that the bound on the relative truncation error in the forward-difference approximation tends to be an increasing function of

h

, while the relative condition error bound is generally a decreasing function of

h

, hence changes in

h

will tend to have opposite effects on these errors (see Gill et al. (1983b)).

The ‘best’ interval

h

is given by

h_{F} = 2 \sqrt{\frac{(1 + |f (x)|) e_{R}}{|Φ|}}

(1)

where

Φ

is an estimate of

f^{''} (x)

, and

e_{R}

is an estimate of the relative error associated with computing the function (see Chapter 8 of Gill et al. (1981)). Given an interval

h

Φ

is defined by the second-order approximation

Φ = \frac{f (x + h) - 2 f (x) + f (x - h)}{h^{2}} .

The decision as to whether a given value of

Φ

is acceptable involves

\hat{c} (Φ)

, the following bound on the relative condition error in

Φ

\hat{c} (Φ) = \frac{4 e_{R} (1 + |f|)}{h^{2} |Φ|}

(When

Φ

is zero,

\hat{c} (Φ)

is taken as an arbitrary large number.)

The procedure selects the interval

h_{ϕ}

(to be used in computing

Φ

) from a sequence of trial intervals

(h_{k})

. The initial trial interval is taken as

\bar{h} = 2 (1 + |x|) \sqrt[4]{e_{R}} .

unless you specify the initial value to be used.

The value of

\hat{c} (Φ)

for a trial value

h_{k}

is defined as ‘acceptable’ if it lies in the interval

[0.0001, 0.01]

. In this case

h_{ϕ}

is taken as

h_{k}

, and the current value of

Φ

is used to compute

h_{F}

from (1). If

\hat{c} (Φ)

is unacceptable, the next trial interval is chosen so that the relative condition error bound will either decrease or increase, as required. If the bound on the relative condition error is too large, a larger interval is used as the next trial value in an attempt to reduce the condition error bound. On the other hand, if the relative condition error bound is too small,

h_{k}

is reduced.

The procedure will fail to produce an acceptable value of

\hat{c} (Φ)

in two situations. Firstly, if

f^{''} (x)

is extremely small, then

\hat{c} (Φ)

may never become small, even for a very large value of the interval. Alternatively,

\hat{c} (Φ)

may never exceed 0.0001, even for a very small value of the interval. This usually implies that

f^{''} (x)

is extremely large, and occurs most often near a singularity.

As a check on the validity of the estimated first derivative, the procedure provides a comparison of the forward-difference approximation computed with

h_{F}

(as above) and the central-difference approximation computed with

h_{ϕ}

. Using the central-difference formula the first derivative can be approximated by

ρ_{c} (f, h) = \frac{f (x + h) - f (x - h)}{2 h}

where

h > 0

. If the values

h_{F}

and

h_{ϕ}

do not display some agreement, neither can be considered reliable.

The approximate Hessian matrix

G

is defined as in Chapter 2 of Gill et al. (1981), by

G_{i j} (x) = \frac{1}{h_{i} h_{j}} (f (x + h_{i} e_{i} + h_{j} e_{j}) - f (x + h_{i} e_{i}) - f (x + h_{j} e_{j}) + f (x)) .

where

h_{j}

is the best forward-difference interval associated with the

j

th component of

f

and

e_{j}

is the vector with unity in the

j

th position and zeros elsewhere.

If you require the gradients and only the diagonal of the Hessian matrix (i.e.,

options . deriv_want = Nag_Grad_HessDiag

; see Section 11.2), nag_opt_estimate_deriv (e04xac) follows a similar procedure to the default case, except that the initial trial interval is taken as

10 \bar{h}

, where

\bar{h} = 2 (1 + |x|) \sqrt{e_{R}}

and the value of

\hat{c} (Φ)

for a trial value

h_{k}

is defined as acceptable if it lies in the interval

[0.001, 0.1]

. The elements of the Hessian diagonal which are returned in this case are the values of

Φ

corresponding to the ‘best’ intervals.

When both function and gradients are available and you require the Hessian matrix (i.e.,

options . deriv_want = Nag_HessFull

; see Section 11.2), nag_opt_estimate_deriv (e04xac) follows a similar procedure to the case above with the exception that the gradient function

g (x)

is substituted for the objective function and so the forward-difference interval for the first derivative of

g (x)

with respect to variable

x_{j}

is computed. The

j

th column of the approximate Hessian matrix is then defined as in Chapter 2 of Gill et al. (1981), by

\frac{g (x + h_{j} e_{j}) - g (x)}{h_{j}}

where

h_{j}

is the best forward-difference interval associated with the

j

th component of

g

4 References

Gill P E, Murray W, Saunders M A and Wright M H (1983a) Documentation for FDCALC and FDCORE Technical Report SOL 83–6 Stanford University

Gill P E, Murray W, Saunders M A and Wright M H (1983b) Computing forward-difference intervals for numerical optimization SIAM J. Sci. Statist. Comput. 4 310–321

Gill P E, Murray W and Wright M H (1981) Practical Optimization Academic Press

5 Arguments

1: $n$ – IntegerInput

On entry: the number

n

of variables.

Constraint:

n \geq 1

2: $x [n]$ – doubleInput

On entry: the point

x

at which derivatives are required.

3: $objfun$ – function, supplied by the userExternal Function

objfun must evaluate the objective function

F (x)

and (optionally) its gradient

g (x) = \frac{\partial F}{\partial x_{j}}

for a specified

n

element vector

x

The specification of objfun is:

void	objfun (Integer n, const double x[], double objf, double g[], Nag_Comm comm)

1: $n$ – IntegerInput

On entry: the number

n

of variables.

2: $x [n]$ – const doubleInput

On entry: the point

x

at which the value of

F

and, if

comm \to flag = 2

, the

\frac{\partial F}{\partial x_{j}}

, are required.

3: $objf$ – double *Output

On exit: objfun must set objf to the value of the objective function

F

at the current point

x

. If it is not possible to evaluate

F

then objfun should assign a negative value to

comm \to flag

; nag_opt_estimate_deriv (e04xac) will then terminate.

4: $g [n]$ – doubleOutput

On exit: if

comm \to flag = 2

on entry, then objfun must set

g [j - 1]

to the value of the first derivative

\frac{\partial F}{\partial x_{j}}

at the current point

x

, for

j = 1, 2, \dots, n

. If it is not possible to evaluate the first derivatives then objfun should assign a negative value to

comm \to flag

; nag_opt_estimate_deriv (e04xac) will then terminate.

comm \to flag = 0

on entry, then g is not referenced.

5: $comm$ – Nag_Comm *

Pointer to structure of type Nag_Comm; the following members are relevant to objfun.

flag – IntegerInput/Output: On entry: $comm \to flag$ will be set to 0 or 2. The value 0 indicates that only $F$ itself needs to be evaluated. The value 2 indicates that both $F$ and its first derivatives must be calculated.

On exit: if objfun resets $comm \to flag$ to a negative number then nag_opt_estimate_deriv (e04xac) will terminate immediately with the error indicator NE_USER_STOP. If fail is supplied to nag_opt_estimate_deriv (e04xac), $fail . errnum$ will be set to the user's setting of $comm \to flag$ .

first – Nag_BooleanInput: On entry: will be set to Nag_TRUE on the first call to objfun and Nag_FALSE for all subsequent calls.

nf – IntegerInput: On entry: the number of evaluations of the objective function; this value will be equal to the number of calls made to objfun (including the current one).

user – double *
iuser – Integer *
p – Pointer: The type Pointer will be void * with a C compiler that defines void * and char * otherwise.

Before calling nag_opt_estimate_deriv (e04xac) these pointers may be allocated memory and initialized with various quantities for use by objfun when called from nag_opt_estimate_deriv (e04xac).

Note: objfun should be thoroughly tested before being used in conjunction with nag_opt_estimate_deriv (e04xac). The array x must not be changed by objfun.

4: $objf$ – double *Output

On exit: the value of the objective function evaluated at the input vector in x.

5: $g [n]$ – doubleOutput

On exit: if

options . deriv_want = Nag_Grad_HessFull

(the default; see Section 11.2) or

options . deriv_want = Nag_Grad_HessDiag

g [j - 1]

contains the best estimate of the first partial derivative for the

j

th variable,

j = 1, 2, \dots, n

. If

options . deriv_want = Nag_HessFull

g [j - 1]

contains the first partial derivative for the

j

th variable as evaluated by objfun.

6: $h_forward [n]$ – doubleInput/Output

On entry: if the optional argument

options . use_hfwd_init = Nag_FALSE

(the default; see Section 11.2), the values contained in h_forward on entry to nag_opt_estimate_deriv (e04xac) are ignored.

options . use_hfwd_init = Nag_TRUE

, h_forward is assumed to contain meaningful values on entry: if

h_forward [j - 1] > 0

then it is used as the initial trial interval for computing the appropriate partial derivative to the

j

th variable,

j = 1, 2, \dots, n

; if

h_forward [j - 1] \leq 0.0

, then the initial trial interval for the

j

th variable is computed by nag_opt_estimate_deriv (e04xac) (see Section 11.2).

On exit:

h_forward [j - 1]

is the best interval found for computing a forward-difference approximation to the appropriate partial derivative for the

j

th variable. If you do not require this information, a NULL pointer may be provided, and nag_opt_estimate_deriv (e04xac) will allocate memory internally to calculate the difference intervals.

Constraint: h_forward must not be NULL if

options . use_hfwd_init = Nag_TRUE

7: $h_central [n]$ – doubleOutput

On exit:

h_central [j - 1]

is the best interval found for computing a central-difference approximation to the appropriate partial derivative for the

j

th variable. If you do not require this information, a NULL pointer may be provided, and nag_opt_estimate_deriv (e04xac) will allocate memory internally to calculate the difference intervals.

8: $hess [n \times tdhess]$ – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

hess [(i - 1) \times tdhess + j - 1]

On exit: if the optional argument

options . deriv_want = Nag_Grad_HessFull

(the default; see Section 11.2) or

options . deriv_want = Nag_HessFull

, the estimated Hessian matrix is contained in the leading

n

n

part of this array. If

options . deriv_want = Nag_Grad_HessDiag

, the

n

elements of the estimated Hessian diagonal are contained in the first row of this array.

9: $tdhess$ – IntegerInput

On entry: the stride separating matrix column elements in the array hess.

Constraint:

tdhess \geq n

10: $deriv_info [n]$ – Nag_DerivInfo *Output

On exit:

deriv_info [j - 1]

contains diagnostic information on the

j

th variable, for

j = 1, 2, \dots, n

$deriv_info [j - 1] = Nag_Deriv_OK$: No unusual behaviour observed in estimating the appropriate derivative.
$deriv_info [j - 1] = Nag_Fun_Constant$: The appropriate function appears to be constant.
$deriv_info [j - 1] = Nag_Fun_LinearOdd$: The appropriate function appears to be linear or odd.
$deriv_info [j - 1] = Nag_2ndDeriv_Large$: The second derivative of the appropriate function appears to be so large that it cannot be reliably estimated (e.g., near a singularity).
$deriv_info [j - 1] = Nag_1stDeriv_Small$: The forward-difference and central-difference estimates of the appropriate first derivatives do not agree to half a decimal place; this usually occurs because the first derivative is small.

A more detailed explanation of these warnings is given in Section 9.1.

11: $options$ – Nag_E04_Opt *Input/Output

On entry/exit: a pointer to a structure of type Nag_E04_Opt whose members are optional arguments for nag_opt_estimate_deriv (e04xac). These structure members offer the means of adjusting some of the argument values of the computation and on output will supply further details of the results. A description of the members of options is given in Section 11.

If any of these optional arguments are required then the structure options should be declared and initialized by a call to nag_opt_init (e04xxc) and supplied as an argument to nag_opt_estimate_deriv (e04xac). However, if the optional arguments are not required the NAG defined null pointer, E04_DEFAULT, can be used in the function call.

12: $comm$ – Nag_Comm *Input/Output

Note: comm is a NAG defined type (see Section 3.2.1.1 in the Essential Introduction).

On entry/exit: structure containing pointers for communication with user-supplied functions; see the description of objfun for details. If you do not need to make use of this communication feature, the null pointer NAGCOMM_NULL may be used in the call to nag_opt_estimate_deriv (e04xac); comm will then be declared internally for use in calls to user-supplied functions.

13: $fail$ – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

5.1 Description of Printed Output

Results from nag_opt_estimate_deriv (e04xac) are printed out by default. The level of printed output can be controlled with the structure members

options . list

and

options . print_deriv

(see Section 11.2). If

options . list = Nag_TRUE

then the argument values to nag_opt_estimate_deriv (e04xac) are listed, whereas printout of results is governed by the value of

options . print_deriv

The default,

options . print_deriv = Nag_D_Print

provides the following line of output for each variable.

j	the index of the variable for which the difference interval has been computed.
X(j)	the value of $x_{j}$ as provided in $x [j - 1]$ .
Fwd diff int	the best interval found for computing a forward-difference approximation to the appropriate partial derivative with respect to $x_{j}$ .
Cent diff int	the best interval found for computing a central-difference approximation to the appropriate partial derivative with respect to $x_{j}$ .
Error est	a bound on the estimated error in the final forward-difference approximation. When $deriv_info [j - 1] = Nag_Fun_Constant$ , Error est is set to zero.
Grad est	best estimate of the first partial derivative with respect to $x_{j}$ .
Hess diag est	best estimate of the second partial derivative with respect to $x_{j}$ .
Nfun	the number of function evaluations used to compute the final difference intervals for $x_{j}$ .
Info	gives diagnostic information for $x_{j}$ . Info will be one of OK, Constant?, Linear or odd?, Large 2nd deriv?, or Small 1st deriv?, corresponding to $deriv_info [j - 1] = Nag_Deriv_OK$ , $Nag_Fun_Constant$ , $Nag_Fun_LinearOdd$ , $Nag_2ndDeriv_Large$ or $Nag_1stDeriv_Small$ , respectively.

6 Error Indicators and Warnings

NE_2_INT_ARG_LT: On entry, $tdhess = 〈value〉$ while $n = 〈value〉$ . These arguments must satisfy $tdhess \geq n$ .
NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument $options . deriv_want$ had an illegal value.

On entry, argument $options . print_deriv$ had an illegal value.
NE_H_FORWARD_NULL: $options . use_hfwd_init = Nag_TRUE$ but argument h_forward is NULL.
NE_INT_ARG_LT: On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .
NE_INVALID_REAL_RANGE_F: Value $〈value〉$ given to $options . f_prec$ is not valid. Correct range is $options . f_prec > 0.0$ .
NE_NOT_APPEND_FILE: Cannot open file $〈string〉$ for appending.
NE_NOT_CLOSE_FILE: Cannot close file $〈string〉$ .
NE_OPT_NOT_INIT: Options structure not initialized.
NE_USER_STOP: User requested termination, user flag value $= 〈value〉$ .
This exit occurs if you set $comm \to flag$ to a negative value in objfun. If fail is supplied, the value of $fail . errnum$ will be the same as your setting of $comm \to flag$ .
NE_WRITE_ERROR: Error occurred when writing to file $〈string〉$ .
NW_DERIV_INFO: On exit, at least one element of the deriv_info array does not contain the value $deriv_info = Nag_Deriv_OK$ . This does not necessarily represent an unsuccessful exit.
See Section 9.1 for information about the possible values which may be returned in deriv_info.

7 Accuracy

nag_opt_estimate_deriv (e04xac) exits with

fail . code = NE_NOERROR

if the algorithm terminated successfully, i.e., the forward-difference estimates of the appropriate first derivatives (computed with the final estimate of the ‘optimal’ forward-difference interval

h_{F}

) and the central-difference estimates (computed with the interval

h_{ϕ}

used to compute the final estimate of the second derivative) agree to at least half a decimal place.

8 Parallelism and Performance

Not applicable.

9 Further Comments

9.1 Diagnostic Information

Diagnostic information is returned via the array argument deriv_info. If

fail . code = NE_NOERROR

on exit then

deriv_info [j - 1] = Nag_Deriv_OK

, for

j = 1, 2, \dots, n

. If

fail . code = NW_DERIV_INFO

on exit, then, for at least one

j

deriv_info [j - 1]

contains one of the following values:

$Nag_Fun_Constant$: The appropriate function appears to be constant. On exit, $h_forward [j - 1]$ is set to the initial trial interval corresponding to a well scaled problem, and Error est in the printed output is set to zero. This value occurs when the estimated relative condition error in the first derivative approximation is unacceptably large for every value of the finite difference interval. If this happens when the function is not constant the initial interval may be too small; in this case, it may be worthwhile to rerun nag_opt_estimate_deriv (e04xac) with larger initial trial interval values supplied in h_forward and with the optional argument $options . use_hfwd_init$ set to Nag_TRUE. This error may also occur if the function evaluation includes an inordinately large constant term or if optional argument $options . f_prec$ is too large.
$Nag_Fun_LinearOdd$: The appropriate function appears to be linear or odd. On exit, $h_forward [j - 1]$ is set to the smallest interval with acceptable bounds on the relative condition error in the forward- and backward-difference estimates. In this case, the estimated relative condition error in the second derivative approximation remained large for every trial interval, but the estimated error in the first derivative approximation was acceptable for at least one interval. If the function is not linear or odd the relative condition error in the second derivative may be decreasing very slowly. It may be worthwhile to rerun nag_opt_estimate_deriv (e04xac) with larger initial trial interval values supplied in h_forward and with $options . use_hfwd_init$ set to Nag_TRUE.
$Nag_2ndDeriv_Large$: The second derivative of the appropriate function appears to be so large that it cannot be reliably estimated (e.g., near a singularity). On exit, $h_forward [j - 1]$ is set to the smallest trial interval.
This value occurs when the relative condition error estimate in the second derivative remained very small for every trial interval.

If the second derivative is not large the relative condition error in the second derivative may be increasing very slowly. It may be worthwhile to rerun nag_opt_estimate_deriv (e04xac) with smaller initial trial interval values supplied in h_forward and with $options . use_hfwd_init$ set to Nag_TRUE. This error may also occur when the given value of the optional argument $options . f_prec$ is not a good estimate of a bound on the absolute error in the appropriate function (i.e., $options . f_prec$ is too small).
$Nag_1stDeriv_Small$: The algorithm terminated with an apparently acceptable estimate of the second derivative. However the forward-difference estimates of the appropriate first derivatives (computed with the final estimate of the ‘optimal’ forward-difference interval) and the central difference estimates (computed with the interval used to compute the final estimate of the second derivative) do not agree to half a decimal place. The usual reason that the forward- and central-difference estimates fail to agree is that the first derivative is small.
If the first derivative is not small, it may be helpful to run nag_opt_estimate_deriv (e04xac) at a different point.

9.2 Timing

Unless the objective function can be evaluated very quickly, the run time will usually be dominated by the time spent in objfun.

To evaluate an acceptable set of finite difference intervals for a well-scaled problem nag_opt_estimate_deriv (e04xac) will use around two function evaluations per variable; in a badly scaled problem, six function evaluations per variable may be needed.

In the default case where gradients and the full Hessian matrix are required (i.e., optional argument

options . deriv_want = Nag_Grad_HessFull

), nag_opt_estimate_deriv (e04xac) performs a further

3 n (n + 1) / 2

function evaluations. If the full Hessian matrix is required, with you supplying both function and gradients (i.e.,

options . deriv_want = Nag_HessFull

), a further

n

function evaluations are performed.

10 Example

The example program computes the gradient vector and Hessian matrix of the following function:

F (x) = {(x_{1} + 10 x_{2})}^{2} + 5 {(x_{3} - x_{4})}^{2} + {(x_{2} - 2 x_{3})}^{4} + 10 {(x_{1} - x_{4})}^{4}

at the point

{(3, - 1, 0, 1)}^{T}

This example shows the use of some optional arguments which are discussed fully in Section 11.

The same objfun is used as in Section 10 and the derivatives are estimated at the same point. The options structure is declared and initialized by nag_opt_init (e04xxc). Two options are set to suppress all printout from nag_opt_estimate_deriv (e04xac):

options . list

is set to Nag_FALSE and

options . print_deriv = Nag_D_NoPrint

options . deriv_want = Nag_Grad_HessDiag

and nag_opt_estimate_deriv (e04xac) is called. The returned function value and estimated derivative values are printed out and

options . deriv_want

is reset to

options . deriv_want = Nag_HessFull

before nag_opt_estimate_deriv (e04xac) is called again. On return, the computed function value and gradient, and estimated Hessian, are printed out.

10.1 Program Text

Program Text (e04xace.c)

10.2 Program Data

None.

10.3 Program Results

Program Results (e04xace.r)

11 Optional Arguments

A number of optional input and output arguments to nag_opt_estimate_deriv (e04xac) are available through the structure argument options, type Nag_E04_Opt. An argument may be selected by assigning an appropriate value to the relevant structure member; those arguments not selected will be assigned default values. If no use is to be made of any of the optional arguments you should use the NAG defined null pointer, E04_DEFAULT, in place of options when calling nag_opt_estimate_deriv (e04xac); the default settings will then be used for all arguments.

Before assigning values to options directly the structure must be initialized by a call to the function nag_opt_init (e04xxc). Values may then be assigned to the structure members in the normal C manner.

Option settings may also be read from a text file using the function nag_opt_read (e04xyc) in which case initialization of the options structure will be performed automatically if not already done. Any subsequent direct assignment to the options structure must not be preceded by initialization.

11.1 Optional Argument Checklist and Default Values

For easy reference, the following list shows the members of options which are valid for nag_opt_estimate_deriv (e04xac) together with their default values where relevant. The number

ε

is a generic notation for machine precision (see nag_machine_precision (X02AJC)).

Boolean list	Nag_TRUE
Nag_DPrintType print_deriv	$Nag_D_Print$
char outfile[80]	stdout
Nag_DWantType deriv_want	$Nag_Grad_HessFull$
Boolean use_hfwd_init	Nag_FALSE
double f_prec	$ε^{0.9}$
double f_prec_used
Integer nf

11.2 Description of the Optional Arguments

list – Nag_Boolean

Default

= Nag_TRUE

On entry: if

options . list = Nag_TRUE

the argument settings in the call to nag_opt_estimate_deriv (e04xac) will be printed.

print_deriv – Nag_DPrintType

Default

= Nag_D_Print

On entry: controls whether printout is produced by nag_opt_estimate_deriv (e04xac). The following values are available:

$Nag_D_NoPrint$	No output.
$Nag_D_Print$	Printout for each variable as described in Section 5.

Constraint:

options . print_deriv = Nag_D_NoPrint

Nag_D_Print

outfile – const char[80]

Default

= stdout

On entry: the name of the file to which results should be printed. If

options . outfile [0] ='
  \0
'

then the stdout stream is used.

deriv_want – Nag_DWantType

Default

= Nag_Grad_HessFull

On entry: specifies which derivatives nag_opt_estimate_deriv (e04xac) should estimate. The following values are available:

$Nag_Grad_HessFull$	Estimate the gradient and full Hessian, with you supplying the objective function via objfun.
$Nag_Grad_HessDiag$	Estimate the gradient and the Hessian diagonal values, with you supplying the objective function via objfun.
$Nag_HessFull$	Estimate the full Hessian, with you supplying the objective function and gradients via objfun.

Constraint:

options . deriv_want = Nag_Grad_HessFull

Nag_Grad_HessDiag

Nag_HessFull

use_hfwd_init – Nag_Boolean

Default

= Nag_FALSE

On entry: if

options . use_hfwd_init = Nag_FALSE

, then nag_opt_estimate_deriv (e04xac) ignores any values supplied on entry in h_forward, and computes the initial trial intervals itself. If

options . use_hfwd_init = Nag_TRUE

, then nag_opt_estimate_deriv (e04xac) uses the forward difference interval provided in

h_forward [j - 1]

as the initial trial interval for computing the appropriate partial derivative to the

j

th variable,

j = 1, 2, \dots, n

; however, if

h_forward [j - 1] \leq 0.0

for some

j

, the initial trial interval for the

j

th variable is computed by nag_opt_estimate_deriv (e04xac).

f_prec – double

Default

= ε^{0.9}

On entry: specifies

e_{R}

, which is intended to measure the accuracy with which the problem function

F

can be computed. The value of

options . f_prec

should reflect the relative precision of

1 + |F (x)|

, i.e., acts as a relative precision when

|F|

is large, and as an absolute precision when

|F|

is small. For example, if

|F (x)|

is typically of order 1000 and the first six significant figures are known to be correct, an appropriate value of

options . f_prec

would be

10^{- 6}

. The default value of

ε^{0.9}

will be appropriate for most simple functions that are computed with full accuracy

A discussion of

e_{R}

is given in Chapter 8 of Gill et al. (1981). If you provide a value of

options . f_prec

which nag_opt_estimate_deriv (e04xac) determines to be either too small or too large , the default value will be used instead and a warning will be output if optional argument

options . print_deriv = Nag_D_Print

. The value actually used is returned in

options . f_prec_used

Constraint:

options . f_prec > 0

f_prec_used – double

On exit: if

fail . code = NE_NOERROR

or NW_DERIV_INFO, or if

options . nf > 1

and

fail . code = NE_USER_STOP

, then

options . f_prec_used

contains the value of

e_{R}

used by nag_opt_estimate_deriv (e04xac). If you supply a value for

options . f_prec

and nag_opt_estimate_deriv (e04xac) considers that the value supplied is neither too large nor too small, then this value will be returned in

options . f_prec_used

; otherwise

options . f_prec_used

will contain the default value,

ε^{0.9}

nf – double

On exit: the number of times the objective function has been evaluated (i.e., number of calls of objfun).

nag_opt_estimate_deriv (e04xac) (PDF version)

e04 Chapter Contents

e04 Chapter Introduction

NAG Library Manual

NAG Library Function Documentnag_opt_estimate_deriv (e04xac)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

5.1 Description of Printed Output

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Diagnostic Information

9.2 Timing

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

11 Optional Arguments

11.1 Optional Argument Checklist and Default Values

11.2 Description of the Optional Arguments

NAG Library Function Document

nag_opt_estimate_deriv (e04xac)