c05azc locates a simple zero of a continuous function in a given interval by using Brent's method, which is a combination of nonlinear interpolation, linear extrapolation and bisection. It uses reverse communication for evaluating the function.

2 Specification

#include <nag.h>

void	c05azc (double x, double y, double fx, double tolx, Nag_ErrorControl ir, double c[], Integer ind, NagError fail)

The function may be called by the names: c05azc, nag_roots_contfn_brent_rcomm or nag_zero_cont_func_brent_rcomm.

3 Description

You must supply x and y to define an initial interval

[a, b]

containing a simple zero of the function

f (x)

(the choice of x and y must be such that

f (x) \times f (y) \leq 0.0

). The function combines the methods of bisection, nonlinear interpolation and linear extrapolation (see Dahlquist and Björck (1974)), to find a sequence of sub-intervals of the initial interval such that the final interval

[x, y]

contains the zero and

| x - y |

is less than some tolerance specified by tolx and ir (see Section 5). In fact, since the intermediate intervals

[x, y]

are determined only so that

f (x) \times f (y) \leq 0.0

, it is possible that the final interval may contain a discontinuity or a pole of

f

(violating the requirement that

f

be continuous). c05azc checks if the sign change is likely to correspond to a pole of

f

and gives an error return in this case.

A feature of the algorithm used by this function is that unlike some other methods it guarantees convergence within about

{(\log_{2} [(b - a) / δ])}^{2}

function evaluations, where

δ

is related to the argument tolx. See Brent (1973) for more details.

c05azc returns to the calling program for each evaluation of

f (x)

. On each return you should set

fx = f (x)

and call c05azc again.

The function is a modified version of procedure ‘zeroin’ given by Brent (1973).

4 References

Brent R P (1973) Algorithms for Minimization Without Derivatives Prentice–Hall

Bus J C P and Dekker T J (1975) Two efficient algorithms with guaranteed convergence for finding a zero of a function ACM Trans. Math. Software 1 330–345

Dahlquist G and Björck Å (1974) Numerical Methods Prentice–Hall

5 Arguments

Note: this function uses reverse communication. Its use involves an initial entry, intermediate exits and re-entries, and a final exit, as indicated by the argument ind. Between intermediate exits and re-entries, all arguments other than fx must remain unchanged.

1: $x$ – double * Input/Output

2: $y$ – double * Input/Output

On initial entry: x and y must define an initial interval

[a, b]

containing the zero, such that

f (x) \times f (y) \leq 0.0

. It is not necessary that

x < y

On intermediate exit: x contains the point at which

f

must be evaluated before re-entry to the function.

On final exit: x and y define a smaller interval containing the zero, such that

f (x) \times f (y) \leq 0.0

, and

| x - y |

satisfies the accuracy specified by tolx and ir, unless an error has occurred. If

fail . code =

NE_PROBABLE_POLE, x and y generally contain very good approximations to a pole; if

fail . code =

NW_TOO_MUCH_ACC_REQUESTED, x and y generally contain very good approximations to the zero (see Section 6). If a point x is found such that

f (x) = 0.0

, on final exit

x = y

(in this case there is no guarantee that x is a simple zero). In all cases, the value returned in x is the better approximation to the zero.

3: $fx$ – double Input

On initial entry: if

ind = 1

, fx need not be set.

ind = - 1

, fx must contain

f (x)

for the initial value of x.

On intermediate re-entry: must contain

f (x)

for the current value of x.

4: $tolx$ – double Input

On initial entry: the accuracy to which the zero is required. The type of error test is specified by ir.

Constraint:

tolx > 0.0

5: $ir$ – Nag_ErrorControl Input

On initial entry: indicates the type of error test.

$ir = Nag_Mixed$: The test is: $| x - y | \leq 2.0 \times tolx \times \max (1.0, | x |)$ .
$ir = Nag_Absolute$: The test is: $| x - y | \leq 2.0 \times tolx$ .
$ir = Nag_Relative$: The test is: $| x - y | \leq 2.0 \times tolx \times | x |$ .

Suggested value:

ir = Nag_Mixed

Constraint:

ir = Nag_Mixed

Nag_Absolute

Nag_Relative

6: $c [17]$ – double Input/Output

On initial entry: if

ind = 1

, no elements of c need be set.

ind = - 1

c [0]

must contain

f (y)

, other elements of c need not be set.

On final exit: is undefined.

7: $ind$ – Integer * Input/Output

On initial entry: must be set to

1

- 1

$ind = 1$: fx and $c [0]$ need not be set.
$ind = - 1$: fx and $c [0]$ must contain $f (x)$ and $f (y)$ respectively.

On intermediate exit: contains

2

3

4

. The calling program must evaluate

f

at x, storing the result in fx, and re-enter c05azc with all other arguments unchanged.

On final exit: contains

0

Constraint: on entry

ind = - 1

1

2

3

4

Note: any values you return to c05azc as part of the reverse communication procedure should not include floating-point NaN (Not a Number) or infinity values, since these are not handled by c05azc. If your code inadvertently does return any NaNs or infinities, c05azc is likely to produce unexpected results.

8: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM: On entry, argument $⟨ value ⟩$ had an illegal value.
NE_INT: On entry, $ind = ⟨ value ⟩$ .
Constraint: $ind = - 1$ , $1$ , $2$ , $3$ or $4$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_NOT_SIGN_CHANGE: On entry, $f (x)$ and $f (y)$ have the same sign with neither equalling $0.0$ : $f (x) = ⟨ value ⟩$ and $f (y) = ⟨ value ⟩$ .
NE_PROBABLE_POLE: The final interval may contain a pole rather than a zero. Note that this error exit is not completely reliable: it may be taken in extreme cases when $[x, y]$ contains a zero, or it may not be taken when $[x, y]$ contains a pole. Both these cases occur most frequently when tolx is large.
NE_REAL: On entry, $tolx = ⟨ value ⟩$ .
Constraint: $tolx > 0.0$ .
NW_TOO_MUCH_ACC_REQUESTED: The tolerance tolx has been set too small for the problem being solved. However, the values x and y returned may well be good approximations to the zero. $tolx = ⟨ value ⟩$ .

7 Accuracy

The accuracy of the final value x as an approximation of the zero is determined by tolx and ir (see Section 5). A relative accuracy criterion (

ir = 2

) should not be used when the initial values x and y are of different orders of magnitude. In this case a change of origin of the independent variable may be appropriate. For example, if the initial interval

[x, y]

is transformed linearly to the interval

[1, 2]

, then the zero can be determined to a precise number of figures using an absolute (

ir = 1

) or relative (

ir = 2

) error test and the effect of the transformation back to the original interval can also be determined. Except for the accuracy check, such a transformation has no effect on the calculation of the zero.

8 Parallelism and Performance

c05azc is not threaded in any implementation.

9 Further Comments

For most problems, the time taken on each call to c05azc will be negligible compared with the time spent evaluating

f (x)

between calls to c05azc.

If the calculation terminates because

f (x) = 0.0

, then on return y is set to x. (In fact,

y = x

on return only in this case and, possibly, when

fail . code =

NW_TOO_MUCH_ACC_REQUESTED.) There is no guarantee that the value returned in x corresponds to a simple root and you should check whether it does. One way to check this is to compute the derivative of