d01 Chapter Contents
d01 Chapter Introduction
NAG Library Manual

# NAG Library Function Documentnag_quad_1d_gen_vec_multi_rcomm (d01rac)

Note: this function uses optional arguments to define choices in the problem specification and in the details of the algorithm. If you wish to use default settings for all of the optional arguments, you need only read Sections 1 to 10 of this document. If, however, you wish to reset some or all of the settings please refer to Section 11 for a detailed description of the specification of the optional arguments.

## 1  Purpose

nag_quad_1d_gen_vec_multi_rcomm (d01rac) is a general purpose adaptive integrator which calculates an approximation to a vector of definite integrals $\mathbf{F}$ over a finite range $\left[a,b\right]$, given the vector of integrands $\mathbf{f}\left(x\right)$.
 $F = ∫ a b fx d x$
If the same subdivisions of the range are equally good for functions ${f}_{1}\left(x\right)$ and ${f}_{2}\left(x\right)$, because ${f}_{1}\left(x\right)$ and ${f}_{2}\left(x\right)$ have common areas of the range where they vary slowly and where they vary quickly, then we say that ${f}_{1}\left(x\right)$ and ${f}_{2}\left(x\right)$ are ‘similar’. nag_quad_1d_gen_vec_multi_rcomm (d01rac) is particularly effective for the integration of a vector of similar functions.

## 2  Specification

 #include #include
 void nag_quad_1d_gen_vec_multi_rcomm (Integer *irevcm, Integer ni, double a, double b, Integer *sid, Integer needi[], double x[], Integer lenx, Integer *nx, const double fm[], Integer ldfm, double dinest[], double errest[], const Integer iopts[], const double opts[], Integer icom[], Integer licom, double com[], Integer lcom, NagError *fail)

## 3  Description

nag_quad_1d_gen_vec_multi_rcomm (d01rac) is an extension to various QUADPACK routines, including QAG, QAGS and QAGP. The extensions made allow multiple integrands to be evaluated simultaneously, using a vectorized interface and reverse communication.
The quadrature scheme employed by nag_quad_1d_gen_vec_multi_rcomm (d01rac) can be chosen by you. Six Gauss–Kronrod schemes are available. The algorithm incorporates a global acceptance criterion (as defined by Malcolm and Simpson (1976)), optionally together with the ε-algorithm (see Wynn (1956)) to perform extrapolation. The local error estimation is described in Piessens et al. (1983).
nag_quad_1d_gen_vec_multi_rcomm (d01rac) is the integration function in the suite of functions nag_quad_1d_gen_vec_multi_rcomm (d01rac) and nag_quad_1d_gen_vec_multi_dimreq (d01rcc). It also uses optional arguments, which can be set and queried using the functions nag_quad_opt_set (d01zkc) and nag_quad_opt_get (d01zlc) respectively. The options available are described in Section 11.
First, the option arrays iopts and opts must be initialized using nag_quad_opt_set (d01zkc). Thereafter any required options must be set before calling nag_quad_1d_gen_vec_multi_rcomm (d01rac), or the function nag_quad_1d_gen_vec_multi_dimreq (d01rcc).
A typical usage of this suite of functions is (in pseudo-code for clarity),
Setup phase
```liopts = 100
lopts = 100
allocate(iopts(liopts),opts(lopts))
d01zkc('initialize = d01rac',iopts,liopts,opts,lopts,fail)
d01zkc('option = value',iopts,liopts,opts,lopts,fail)
...
d01rcc(ni,lenxrq,ldfmrq,sdfmrq,licmin,licmax,lcmin,lcmax,
iopts,opts,fail)
lenx = lenxrq
ldfm = ldfmrq
sdfm = sdfmrq
licom = licmax
lcom = lcmax
allocate(icom(licom),com(lcom),x(lenx),fm(ldfm,sdfm),needi(ni),
dinest(ni),errest(ni))
```
Solve phase
```irevcm = 1
while irevcm≠0
d01rac(irevcm,ni,a,b,sid,needi,x,lenx,nx,fm,ldfm,
dinest,errest,iopts,opts,icom,licom,com,
lcom,fail)
select case(irevcm)
case(11)
Initial solve phase
evaluate fm(1:ni,1:nx)
case(12)
evaluate fm(needi(1:ni)=1,1:nx)
case(0)
investigate fail
end select
end while
```
Diagnostic phase
```d01zlc('option',ivalue,rvalue,cvalue,optype,iopts,opts,fail)
...
```
During the initial solve phase, the first estimation of the definite integral and error estimate is constructed over the interval $\left[a,b\right]$. This will have been divided into ${s}_{\mathit{pri}}$ level $1$ segments, where ${s}_{\mathit{pri}}$ is the number of ${\mathbf{Primary Divisions}}$, and will use any provided break-points if ${\mathbf{Primary Division Mode}}='\mathrm{MANUAL}'$.
Once a complete integral estimate over $\left[a,b\right]$ is available, i.e., after all the estimates for the level 1 segments have been evaluated, the function enters the adaptive phase. The estimated errors are tested against the requested tolerances ${\epsilon }_{a}$ and ${\epsilon }_{r}$, corresponding to the ${\mathbf{Absolute Tolerance}}$ and ${\mathbf{Relative Tolerance}}$ respectively. Should this test fail, and additional subdivision be allowed, a segment is selected for subdivision, and is subsequently replaced by two new segments at the next level of refinement. How this segment is chosen may be altered by setting ${\mathbf{Prioritize Error}}$ to either favour the segment with the maximum error, or the segment with the lowest level supporting an unacceptable (although potentially non-maximal) error. Up to ${\mathrm{max}}_{\mathit{sdiv}}$ subdivisions are allowed if sufficient memory is provided, where ${\mathrm{max}}_{\mathit{sdiv}}$ is the value of ${\mathbf{Maximum Subdivisions}}$.
Once a sufficient number of error estimates have been constructed for a particular integral, the function may optionally use ${\mathbf{Extrapolation}}$ to attempt to accelerate convergence. This may significantly lower the amount of work required for a given integration. To minimize the risk of premature convergence from extrapolation, a safeguard ${\epsilon }_{\mathit{safe}}$ can be set using ${\mathbf{Extrapolation Safeguard}}$, and the extrapolated solution will only be considered if ${\epsilon }_{\mathit{safe}}{\epsilon }_{q}\le {\epsilon }_{\mathit{ex}}$, where ${\epsilon }_{q}$ and ${\epsilon }_{\mathit{ex}}$ are the estimated error directly from the quadrature and from the extrapolation respectively. If extrapolation is successful for the computation of integral $j$, the extrapolated solution will be returned in ${\mathbf{dinest}}\left[j-1\right]$ on completion of nag_quad_1d_gen_vec_multi_rcomm (d01rac). Otherwise the direct solution will be returned in ${\mathbf{dinest}}\left[j-1\right]$. This is indicated by the value of ${\mathbf{needi}}\left[j-1\right]$ on completion.

## 4  References

Malcolm M A and Simpson R B (1976) Local versus global strategies for adaptive quadrature ACM Trans. Math. Software 1 129–146
Piessens R (1973) An algorithm for automatic integration Angew. Inf. 15 399–401
Piessens R, de Doncker–Kapenga E, Überhuber C and Kahaner D (1983) QUADPACK, A Subroutine Package for Automatic Integration Springer–Verlag
Wynn P (1956) On a device for computing the ${e}_{m}\left({S}_{n}\right)$ transformation Math. Tables Aids Comput. 10 91–96

## 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 irevcm. Between intermediate exits and re-entries, all arguments other than irevcm, needi and fm must remain unchanged.
Where ${\mathbf{FM}}\left(j,i\right)$ appears in this document it refers to the array element ${\mathbf{fm}}\left[\left(i-1\right)×{\mathbf{ldfm}}+j-1\right]$.
1:     irevcmInteger *Input/Output
On initial entry: ${\mathbf{irevcm}}=1$.
${\mathbf{irevcm}}=1$
Sets up data structures in icom and com and starts a new integration.
Constraint: ${\mathbf{irevcm}}=1$ on initial entry.
On intermediate exit: ${\mathbf{irevcm}}=11$ or $12$.
irevcm requests the integrands ${f}_{j}\left({x}_{i}\right)$ be evaluated for all required $j\in 1,\dots ,{n}_{i}$ as indicated by needi, and at all the points ${x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,{n}_{x}$. Abscissae ${x}_{i}$ are provided in ${\mathbf{x}}\left[i-1\right]$ and ${f}_{j}\left({x}_{i}\right)$ must be returned in ${\mathbf{FM}}\left(j,i\right)$.
During the initial solve phase:
${\mathbf{irevcm}}=11$
Function values are required to construct the initial estimates of the definite integrals.
If ${\mathbf{needi}}\left[j-1\right]=1$, ${f}_{j}\left({x}_{i}\right)$ must be supplied in ${\mathbf{FM}}\left(j,i\right)$. This will be the case unless you have abandoned the evaluation of specific integrals on a previous call.
If ${\mathbf{needi}}\left[j-1\right]=0$, you have previously abandoned the evaluation of integral $j$, and hence should not supply the value of ${f}_{j}\left({x}_{i}\right)$.
dinest and errest contain incomplete information during this phase. As such you should not abandon the evaluation of any integrals during this phase unless you do not require their estimate.
If irevcm is set to a negative value during this phase, ${\mathbf{needi}}\left[\mathit{j}-1\right]$, for $\mathit{j}=1,2,\dots ,{n}_{i}$, will be set to this negative value and ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_USER_STOP will be returned.
${\mathbf{irevcm}}=12$
Function values are required to improve the estimates of the definite integrals.
If ${\mathbf{needi}}\left[j-1\right]=0$, any evaluation of ${f}_{j}\left({x}_{i}\right)$ will be discarded, so there is no need to provide them.
If ${\mathbf{needi}}\left[j-1\right]=1$, ${f}_{j}\left({x}_{i}\right)$ must be provided in ${\mathbf{FM}}\left(j,i\right)$.
If ${\mathbf{needi}}\left[j-1\right]=2$, $3$ or $4$, the current error estimate of integral $j$ does not require integrand $j$ to be evaluated and provided in ${\mathbf{FM}}\left(j,i\right)$. Should you choose to, integrand $j$ can be evaluated in which case ${\mathbf{needi}}\left[j-1\right]$ must be set to $1$.
dinest and errest contain complete information during this phase.
If irevcm is set to a negative value during this phase ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_ACCURACY or NE_QUAD_BAD_SUBDIV_INT will be returned and the elements of needi will reflect the current state of the adaptive process.
On intermediate re-entry: irevcm should normally be left unchanged. However, if irevcm is set to a negative value, nag_quad_1d_gen_vec_multi_rcomm (d01rac) will terminate, (see ${\mathbf{irevcm}}=11$ and ${\mathbf{irevcm}}=12$ above).
On final exit: ${\mathbf{irevcm}}=0$.
${\mathbf{irevcm}}=0$
Indicates the algorithm has completed.
2:     niIntegerInput
On entry: ${n}_{i}$, number of integrands.
On entry: $a$, the lower bound of the domain.
4:     bdoubleInput
On entry: $b$, the upper bound of the domain.
If $\left|b-a\right|<10\epsilon$, where $\epsilon$ is the machine precision (see nag_machine_precision (X02AJC)), then nag_quad_1d_gen_vec_multi_rcomm (d01rac) will return ${\mathbf{dinest}}\left[\mathit{j}-1\right]={\mathbf{errest}}\left[\mathit{j}-1\right]=0.0$, for $\mathit{j}=1,2,\dots ,{n}_{i}$.
5:     sidInteger *Output
On intermediate exit: sid identifies a specific set of abscissae, $\mathbf{x}$, returned during the integration process. When a new set of abscissae are generated the value of sid is incremented by $1$. Advanced users may store calculations required for an identified set $\mathbf{x}$, and reuse them should nag_quad_1d_gen_vec_multi_rcomm (d01rac) return the same value of sid, i.e., the same set of abscissae was used.
6:     needi[ni]IntegerInput/Output
On initial entry: need not be set.
On intermediate exit: ${\mathbf{needi}}\left[j-1\right]$ indicates what action must be taken for integral $j=1,2,\dots {n}_{i}$ (see irevcm).
${\mathbf{needi}}\left[j-1\right]=0$
Do not provide ${f}_{j}\left({x}_{i}\right)$. Any provided values will be ignored.
${\mathbf{needi}}\left[j-1\right]=1$
The values ${f}_{j}\left({x}_{\mathit{i}}\right)$ must be provided in ${\mathbf{FM}}\left(j,\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,{n}_{x}$.
${\mathbf{needi}}\left[j-1\right]=2$
The values ${f}_{j}\left({x}_{i}\right)$ are not required, however the error estimate for integral $j$ is still above the requested tolerance. If you wish to provide values for the evaluation of integral $j$, set ${\mathbf{needi}}\left[j-1\right]=1$, and supply ${f}_{j}\left({x}_{\mathit{i}}\right)$ in ${\mathbf{FM}}\left(j,\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,{n}_{x}$.
${\mathbf{needi}}\left[j-1\right]=3$
The error estimate for integral $j$ cannot be improved to below the requested tolerance directly, either because no more new splits may be performed due to exhaustion, or due to the detection of extremely bad integrand behaviour. However, providing the values ${f}_{j}\left({x}_{i}\right)$ may still lead to some improvement, and may lead to an acceptable error estimate indirectly using Wynn's epsilon algorithm. If you wish to provide values for the evaluation of integral $j$, set ${\mathbf{needi}}\left[j-1\right]=1$, and supply ${f}_{j}\left({x}_{\mathit{i}}\right)$ in ${\mathbf{FM}}\left(j,\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,{n}_{x}$.
${\mathbf{needi}}\left[j-1\right]=4$
The error estimate of integral $j$ is below the requested tolerance. If you believe this to be false, if for example the result in ${\mathbf{dinest}}\left[j-1\right]$ is greatly different to what you may expect, you may force the algorithm to re-evaluate this conclusion by including the evaluations of integrand $j$ at ${x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,{n}_{x}$, and setting ${\mathbf{needi}}\left[j-1\right]=1$. Integral and error estimation will be performed again during the next iteration.
On intermediate re-entry: ${\mathbf{needi}}\left[j-1\right]$ may be used to indicate what action you have taken for integral $j$.
${\mathbf{needi}}\left[j-1\right]=1$
You have provided the values ${f}_{j}\left({x}_{\mathit{i}}\right)$ in ${\mathbf{FM}}\left(j,\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,{n}_{x}$.
${\mathbf{needi}}\left[j-1\right]<0$
You are abandoning the evaluation of integral $j$. The current values of ${\mathbf{dinest}}\left[j-1\right]$ and ${\mathbf{errest}}\left[j-1\right]$ will be returned on final completion.
Otherwise you have not provided the value ${f}_{j}\left({x}_{i}\right)$.
On final exit: ${\mathbf{needi}}\left[j-1\right]$ indicates the final state of integral $j$.
${\mathbf{needi}}\left[j-1\right]=0$
The error estimate for ${F}_{j}$ is below the requested tolerance.
${\mathbf{needi}}\left[j-1\right]=1$
The error estimate for ${F}_{j}$ is below the requested tolerance after extrapolation.
${\mathbf{needi}}\left[j-1\right]=2$
The error estimate for ${F}_{j}$ is above the requested tolerance.
${\mathbf{needi}}\left[j-1\right]=3$
The error estimate for ${F}_{j}$ is above the requested tolerance, and extremely bad behaviour of integral $j$ has been detected.
${\mathbf{needi}}\left[j-1\right]<0$
You prohibited further evaluation of integral $j$.
7:     x[lenx]doubleInput/Output
On initial entry: if ${\mathbf{Primary Division Mode}}='\mathrm{AUTOMATIC}'$, x need not be set. This is the default behaviour.
If ${\mathbf{Primary Division Mode}}='\mathrm{MANUAL}'$, x is used to supply a set of initial ‘break-points’ inside the domain of integration. Specifically, ${\mathbf{x}}\left[i-1\right]$ must contain a break-point ${x}_{\mathit{i}}^{0}$, for $\mathit{i}=1,2,\dots ,\left({s}_{\mathit{pri}}-1\right)$, where ${s}_{\mathit{pri}}$ is the number of ${\mathbf{Primary Divisions}}$.
Constraint: if break-points are supplied, ${x}_{\mathit{i}}^{0}\in \left(a,b\right)$, $\left|{x}_{\mathit{i}}^{0}-a\right|>10.0\epsilon$, $\left|{x}_{\mathit{i}}^{0}-b\right|>10.0\epsilon$, for $\mathit{i}=1,2,\dots ,\left({s}_{\mathit{pri}}-1\right)$.
On intermediate exit: ${\mathbf{x}}\left[\mathit{i}-1\right]$ is the abscissa ${x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,{n}_{x}$, at which the appropriate integrals must be evaluated.
8:     lenxIntegerInput
On entry: the dimension of the array x. Currently ${\mathbf{lenx}}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(122,{s}_{\mathit{pri}}-1\right)$ will be sufficient for all cases.
Constraint: ${\mathbf{lenx}}\ge \mathit{lenxrq}$, where $\mathit{lenxrq}$ is dependent upon the options currently set (see Section 11). $\mathit{lenxrq}$ is returned as lenxrq from nag_quad_1d_gen_vec_multi_dimreq (d01rcc).
9:     nxInteger *Input/Output
On initial entry: need not be set.
On intermediate exit: ${n}_{x}$, the number of abscissae at which integrands are required.
On intermediate re-entry: must not be changed.
10:   fm[$\mathit{dim}$]const doubleInput
Note: the dimension, dim, of the array fm must be at least ${\mathbf{ldfm}}×\mathit{sdfmrq}$, where $\mathit{sdfmrq}$ is dependent upon ${n}_{i}$ and the options currently set. $\mathit{sdfmrq}$ is returned as sdfmrq from nag_quad_1d_gen_vec_multi_dimreq (d01rcc). If default options are chosen, $\mathit{sdfmrq}=\mathit{lenxrq}$.
On initial entry: need not be set.
On intermediate re-entry: if indicated by ${\mathbf{needi}}\left[j-1\right]$ you must supply the values ${f}_{j}\left({x}_{i}\right)$ in ${\mathbf{FM}}\left(\mathit{j},\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,{n}_{x}$ and $\mathit{j}=1,2,\dots ,{n}_{i}$.
11:   ldfmIntegerInput
On entry: the stride separating matrix row elements in the array fm.
Constraint: ${\mathbf{ldfm}}\ge \mathit{ldfmrq}$, where $\mathit{ldfmrq}$ is dependent upon ${n}_{i}$ and the options currently set. $\mathit{ldfmrq}$ is returned as ldfmrq from nag_quad_1d_gen_vec_multi_dimreq (d01rcc). If default options are chosen, $\mathit{ldfmrq}={n}_{i}$, implying ${\mathbf{ldfm}}\ge {\mathbf{ni}}$.
12:   dinest[ni]doubleInput/Output
${\mathbf{dinest}}\left[j-1\right]$ contains the current estimate of the definite integral ${F}_{j}$.
On initial entry: need not be set.
On intermediate re-entry: must not be altered.
On exit: contains the current estimates of the ni integrals. If ${\mathbf{irevcm}}=0$, this will be the final solution.
13:   errest[ni]doubleInput/Output
${\mathbf{errest}}\left[j-1\right]$ contains the current error estimate of the definite integral ${F}_{j}$.
On initial entry: need not be set.
On intermediate re-entry: must not be altered.
On exit: contains the current error estimates for the ni integrals. If ${\mathbf{irevcm}}=0$, errest contains the final error estimates of the ni integrals.
14:   iopts[$100$]const IntegerCommunication Array
15:   opts[$100$]const doubleCommunication Array
The arrays iopts and opts MUST NOT be altered between calls to any of the functions nag_quad_1d_gen_vec_multi_rcomm (d01rac), nag_quad_1d_gen_vec_multi_dimreq (d01rcc)nag_quad_opt_set (d01zkc) and nag_quad_opt_get (d01zlc).
16:   icom[licom]IntegerCommunication Array
icom contains details of the integration procedure, including information on the integration of the ${n}_{i}$ integrals over individual segments. This data is stored sequentially in the order that segments are created. For further information see Section 9.1.
17:   licomIntegerInput
On entry: the dimension of the array icom.
Constraint: ${\mathbf{licom}}\ge \mathit{licmin}$, where $\mathit{licmin}$ is dependent upon ni and the current options set. $\mathit{licmin}$ is returned as licmin from nag_quad_1d_gen_vec_multi_dimreq (d01rcc). If the default options are set, then $\mathit{licmin}=55+6×{\mathbf{ni}}$. Larger values than $\mathit{licmin}$ are recommended if you anticipate that any integrals will require the domain to be further subdivided.
The maximum value that may be required, $\mathit{licmax}$, is returned as licmax from nag_quad_1d_gen_vec_multi_dimreq (d01rcc). If default options are chosen, except for possibly increasing the value of ${s}_{\mathit{pri}}$, then $\mathit{licmax}=50+5×{\mathbf{ni}}+\left({s}_{\mathit{pri}}+100\right)×\left(5+{\mathbf{ni}}\right)$.
18:   com[lcom]doubleCommunication Array
com contains details of the integration procedure, including information on the integration of the ${n}_{i}$ integrals over individual segments. This data is stored sequentially in the order that segments are created. For further information see Section 9.1.
19:   lcomIntegerInput
On entry: the dimension of the array com.
Constraint: ${\mathbf{lcom}}>\mathit{lcmin}$, where $\mathit{lcmin}$ is dependent upon ni, ${s}_{\mathit{pri}}$ and the current options set. $\mathit{lcmin}$ is returned as lcmin from nag_quad_1d_gen_vec_multi_dimreq (d01rcc). If default options are set, then $\mathit{lcmin}=96+12×{\mathbf{ni}}$. Larger values are recommended if you anticipate that any integrals will require the domain to be further subdivided.
Given the current options and arguments, the maximum value, $\mathit{lcmax}$, of lcom that may be required, is returned as lcmax from nag_quad_1d_gen_vec_multi_dimreq (d01rcc). If default options are chosen, $\mathit{lcmax}=94+9×{\mathbf{ni}}+⌈{\mathbf{ni}}/2⌉+\left({s}_{\mathit{pri}}+100\right)×\left(2+⌈{\mathbf{ni}}/2⌉+2×{\mathbf{ni}}\right)$.
20:   failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

## 6  Error Indicators and Warnings

NE_ACCURACY
At least one error estimate exceeded the requested tolerances.
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_INT
On entry, ${\mathbf{irevcm}}=⟨\mathit{\text{value}}⟩$.
On entry, ${\mathbf{ni}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{ni}}\ge 1$.
NE_INT_2
${\mathbf{ldfm}}<\mathit{ldfmrq}$. If default options are chosen, this implies ${\mathbf{ldfm}}<{\mathbf{ni}}$.
On entry, ${\mathbf{ldfm}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{ldfm}}\ge ⟨\mathit{\text{value}}⟩$.
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.
NE_INVALID_ARRAY
On entry, one of icom and com has become corrupted.
NE_INVALID_OPTION
Either the option arrays iopts and opts have not been initialized for nag_quad_1d_gen_vec_multi_rcomm (d01rac), or they have become corrupted.
Extremely bad behaviour was detected for at least one integral.
Extremely bad behaviour was detected for at least one integral. At least one other integral error estimate was above the requested tolerance.
On entry, ${\mathbf{Primary Division Mode}}='\mathrm{MANUAL}'$ and at least one supplied break-point in x is outside of the domain of integration.
NE_TOO_SMALL
lcom is insufficient for additional subdivision.
On entry, ${\mathbf{lcom}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{lcom}}\ge ⟨\mathit{\text{value}}⟩$.
lenx is insufficient for the chosen options.
On entry, ${\mathbf{lenx}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{lenx}}\ge ⟨\mathit{\text{value}}⟩$.
licom is insufficient for additional subdivision.
On entry, ${\mathbf{licom}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{licom}}\ge ⟨\mathit{\text{value}}⟩$.
NE_USER_STOP
Evaluation of all integrals has been stopped during the initial phase.

## 7  Accuracy

nag_quad_1d_gen_vec_multi_rcomm (d01rac) cannot guarantee, but in practice usually achieves, the following accuracy for each integral ${F}_{j}$:
 $Fj - dinest[j-1] ≤ tol$
where
 $tol = maxεa, εr × Fj$
${\epsilon }_{a}$ and ${\epsilon }_{r}$ are the error tolerances ${\mathbf{Absolute Tolerance}}$ and ${\mathbf{Relative Tolerance}}$ respectively. Moreover, it returns errest, the entries of which in normal circumstances satisfy,
 $Fj - dinest[j-1] ≤ errest[j-1] ≤ tol .$

## 8  Parallelism and Performance

nag_quad_1d_gen_vec_multi_rcomm (d01rac) 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.

The time required by nag_quad_1d_gen_vec_multi_rcomm (d01rac) is usually dominated by the time required to evaluate the values of the integrands ${f}_{j}$.
nag_quad_1d_gen_vec_multi_rcomm (d01rac) will be most efficient if any badly behaved integrands provided have irregularities over similar subsections of the domain. For example, evaluation of the integrals,
 $∫ 0 1 logx x-12 x2 dx$
will be quite efficient, as the irregular behaviour of the first two integrands is at $x=0$. On the contrary, the evaluation of the integrals,
 $∫ 0 1 logx log1-x dx$
will be less efficient, as the two integrands have singularities at opposite ends of the domain, which will result in subdivisions which are only of use to one integrand. In such cases, it will be more efficient to use two sets of calls to nag_quad_1d_gen_vec_multi_rcomm (d01rac).
nag_quad_1d_gen_vec_multi_rcomm (d01rac) will flag extremely bad behaviour if a sub-interval $\stackrel{-}{\mathit{k}}$ with bounds $\left[{a}_{\stackrel{-}{\mathit{k}}},{b}_{\stackrel{-}{\mathit{k}}}\right]$ satisfying $\left|{b}_{\stackrel{-}{\mathit{k}}}-{a}_{\stackrel{-}{\mathit{k}}}\right|<\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\delta }_{a},{\delta }_{r}×\left|b-a\right|\right)$ has a local error estimate greater than the requested tolerance for at least one integral. The values ${\delta }_{a}$ and ${\delta }_{r}$ can be set through the optional arguments ${\mathbf{Absolute Interval Minimum}}$ and ${\mathbf{Relative Interval Minimum}}$ respectively.

### 9.1  Details of the Computation

This section is recommended for expert users only. It describes the contents of the arrays com and icom upon exit from nag_quad_1d_gen_vec_multi_rcomm (d01rac) with ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR, NE_ACCURACY or NE_QUAD_BAD_SUBDIV_INT, and provided at least one iteration completed, failure due to insufficient licom or lcom.
The arrays icom and com contain details of the integration, including various scalars, one-dimensional arrays, and (effectively) two-dimensional arrays. The dimensions of these arrays vary depending on the arguments and options used and the progress of the algorithm. Here we describe some of these details, including how and where they are stored in icom and com.
Scalar quantities:
The indices in icom for including the following scalars are available via query only options, see Section 11.2. For example, ${I}_{\mathit{ldi}}$ is the integer value returned by the option ${\mathbf{Index LDI}}$. Note the indices returned start at $1$ and so the corresponding zero based indices require $1$ to be subtracted as shown below. This is also true for the location of arrays within icom and com and consequently the indices of the elements of the one- and two-dimensional arrays must be modified as detailed below.
 $\mathit{ldi}$ The leading dimension of the two-dimensional integer arrays stored in icom detailed below. $\mathit{ldi}={\mathbf{icom}}\left[{I}_{\mathit{ldi}}-1\right]$. $\mathit{ldr}$ The leading dimension of the two-dimensional real arrays stored in com detailed below. $\mathit{ldr}={\mathbf{icom}}\left[{I}_{\mathit{ldr}}-1\right]$. $\mathit{nsdiv}$ The number of segments that have been subdivided during the adaptive process. $\mathit{nsdiv}={\mathbf{icom}}\left[{I}_{\mathit{nsdiv}}-1\right]$. $\mathit{nseg}$ The total number of segments formed. $\mathit{nseg}=2\mathit{nsdiv}+{s}_{\mathit{pri}}$. $\mathit{nseg}={\mathbf{icom}}\left[{I}_{\mathit{nseg}}-1\right]$. $\mathit{dsp}$ The reference of the first element of the array $\mathit{ds}$ stored in com. $\mathit{dsp}={\mathbf{icom}}\left[{I}_{\mathit{dsp}}-1\right]$. $\mathit{esp}$ The reference of the first element of the array $\mathit{es}$ stored in com. $\mathit{esp}={\mathbf{icom}}\left[{I}_{\mathit{esp}}-1\right]$. $\mathit{evalsp}$ The reference of the first element of the array $\mathit{evals}$ stored in icom. $\mathit{evalsp}={\mathbf{icom}}\left[{I}_{\mathit{evalsp}}-1\right]$. $\mathit{fcp}$ The reference of the first element of the array $\mathit{fcount}$ stored in icom. $\mathit{fcp}={\mathbf{icom}}\left[{I}_{\mathit{fcp}}-1\right]$. $\mathit{sinforp}$ The reference of the first element of the array $\mathit{sinfor}$ stored in com. $\mathit{sinforp}={\mathbf{icom}}\left[{I}_{\mathit{sinforp}}-1\right]$. $\mathit{sinfoip}$ The reference of the first element of the array $\mathit{sinfoi}$ stored in icom. $\mathit{sinfoip}={\mathbf{icom}}\left[{I}_{\mathit{sinfoip}}-1\right]$.
One-dimensional arrays:
$\mathit{fcount}\left[{n}_{i}\right]$
$\mathit{fcount}\left[0\right]={\mathbf{icom}}\left[\mathit{fcp}-1\right]$.
$\mathit{fcount}\left[j-1\right]$ contains the number of different approximations of integral $\mathit{j}$ calculated, for $\mathit{j}=1,2,\dots ,{n}_{i}$.
Two-dimensional arrays:
$\mathit{sinfoi}\left[5×\mathit{nseg}\right]$
$\mathit{sinfoi}\left[0\right]={\mathbf{icom}}\left[\mathit{sinfoip}-1\right]$.
$\mathit{sinfoi}$ contains information about the hierarchy of splitting.
$\mathit{sinfoi}\left[\left(\mathit{k}-1\right)×\mathit{ldi}\right]$ contains the split identifier for segment $\mathit{k}$, for $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
$\mathit{sinfoi}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+1\right]$ contains the parent segment number of segment $\mathit{k}$ (i.e., the segment was split to create segment $\mathit{k}$), for $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
$\mathit{sinfoi}\left[\left(k-1\right)×\mathit{ldi}+2\right]$ and $\mathit{sinfoi}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+3\right]$ contain the segment numbers of the two child segments formed from segment $\mathit{k}$, if segment $\mathit{k}$ has been split. If segment $\mathit{k}$ has not been split, these will be negative.
$\mathit{sinfoi}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+4\right]$ contains the level at which the segment exists, corresponding to ${n}_{a}+1$, where ${n}_{a}$ is the number of ancestor segments of segment $\mathit{k}$, for $\mathit{k}=1,2,\dots ,\mathit{nseg}$. A negative level indicates that segment $\mathit{k}$ will not be split further, the level is then given by the absolute value of $\mathit{sinfoi}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+4\right]$.
$\mathit{sinfor}\left[2×\mathit{nseg}\right]$
$\mathit{sinfor}\left[0\right]={\mathbf{com}}\left[\mathit{sinforp}-1\right]$.
$\mathit{sinfor}$ contains the bounds of each segment.
$\mathit{sinfor}\left[\left(\mathit{k}-1\right)×\mathit{ldr}\right]$ contains the lower bound of segment $\mathit{k}$, for $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
$\mathit{sinfor}\left[\left(\mathit{k}-1\right)×\mathit{ldr}+1\right]$ contains the upper bound of segment $\mathit{k}$, for $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
$\mathit{evals}\left[{n}_{i}×\mathit{nseg}\right]$
$\mathit{evals}\left[0\right]={\mathbf{icom}}\left[\mathit{evalsp}-1\right]$.
$\mathit{evals}$ contains information to indicate whether an estimate of the integral $\mathit{j}$ has been obtained over segment $\mathit{k}$, and if so whether this evaluation still contributes to the direct estimate of ${F}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,{n}_{i}$ and $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
$\mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+\mathit{j}-1\right]=0$ indicates that integral $j$ has not been evaluated over segment $\mathit{k}$.
$\mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+\mathit{j}-1\right]=1$ indicates that integral $j$ has been evaluated over segment $\mathit{k}$, and that this evaluation contributes to the direct estimate of ${F}_{j}$.
$\mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+\mathit{j}-1\right]=2$ indicates that integral $j$ has been evaluated over segment $\mathit{k}$, that this evaluation contributes to the direct estimate of ${F}_{j}$, and that you have requested no further evaluation of this integral at this segment by setting ${\mathbf{needi}}\left[j-1\right]<0$.
$\mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+\mathit{j}-1\right]=3$ indicates that integral $j$ has been evaluated over segment $\mathit{k}$, and this evaluation no longer contributes to the direct estimate of ${F}_{j}$.
$\mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+\mathit{j}-1\right]=4$ indicates that integral $j$ has been evaluated over segment $\mathit{k}$, that this evaluation contributes to the direct estimate of ${F}_{j}$, and that this segment is too small for any further splitting to be performed. Integral $j$ also has a local error estimate over this segment above the requested tolerance. Such segments cause nag_quad_1d_gen_vec_multi_rcomm (d01rac) to return ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_QUAD_BAD_SUBDIV_INT, indicating extremely bad behaviour.
$\mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+\mathit{j}-1\right]=5$ indicates that integral $j$ has been evaluated over segment $\mathit{k}$, that this evaluation contributes to the direct estimate of ${F}_{j}$, and that this segment is too small for any further splitting to be performed. The local error estimate is however below the requested tolerance.
$\mathit{ds}\left[{n}_{i}×\mathit{nseg}\right]$
$\mathit{ds}\left[0\right]={\mathbf{com}}\left[\mathit{dsp}-1\right]$.
$\mathit{ds}\left[\left(\mathit{k}-1\right)×\mathit{ldr}+j-1\right]$ contains the definite integral estimate of the $j$th integral over the $\mathit{k}$th segment, ${\mathit{ds}}_{j,\mathit{k}}$, provided it has been evaluated, for $\mathit{j}=1,2,\dots ,{n}_{i}$ and $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
$\mathit{es}\left[{n}_{i}×\mathit{nseg}\right]$
$\mathit{es}\left[0\right]={\mathbf{com}}\left[\mathit{esp}-1\right]$.
$\mathit{es}\left[\left(\mathit{k}-1\right)×\mathit{ldr}+j-1\right]$ contains the definite integral error estimate of the $j$th integral over the $\mathit{k}$th segment, ${\mathit{es}}_{j,\mathit{k}}$, provided it has been evaluated, for $\mathit{j}=1,2,\dots ,{n}_{i}$ and $\mathit{k}=1,2,\dots ,\mathit{nseg}$.
For each integral $j$, the direct approximation ${D}_{j}$ of ${F}_{j}$, and its error estimate ${E}_{j}$, may be constructed as,
 $Fj ≈ Dj = ∑ Kj dsj,k , Fj-Dj ≈ Ej = ∑ Kj esj,k ,$
where ${K}_{j}$ is the set of all contributing segments, ${K}_{j}=\left\{\mathit{k}\mid \mathit{evals}\left[\left(\mathit{k}-1\right)×\mathit{ldi}+j-1\right]=1\text{, ​}2\text{, ​}4\text{​ or ​}5\text{, ​}1\le \mathit{k}\le \mathit{nseg}\right\}$. ${D}_{j}$ will have been returned in ${\mathbf{dinest}}\left[j-1\right]$, unless extrapolation was successful, as indicated by ${\mathbf{needi}}\left[j-1\right]$.
Similarly, ${E}_{j}$ will have been returned in ${\mathbf{errest}}\left[j-1\right]$ unless extrapolation was successful, in which case the error estimate from the extrapolation will have been returned. If for a given integral $j$ one or more contributing segments have unacceptable error estimates, it may be possible to improve the direct approximation by replacing the contributions from these segments with more accurate estimates should these be calculable by some means. Indeed for any segment $\stackrel{-}{\mathit{k}}\in \mathit{k}$, with lower bound ${a}_{\stackrel{-}{\mathit{k}}}=\mathit{sinfor}\left(1,\stackrel{-}{\mathit{k}}\right)$ and upper bound ${b}_{\stackrel{-}{\mathit{k}}}=\mathit{sinfor}\left(2,\stackrel{-}{\mathit{k}}\right)$, one may alter the direct approximation ${D}_{j}$ by the following,
 $ds j,k- new ≈ ∫ ak- bk- fjx ​ ​ dx Dj = ∑Kj ds j,k - ds j , k- + ds j,k- new .$
The error estimate ${E}_{j}$ may be altered similarly.

## 10  Example

This example integrates
 $F = ∫ 0 π x sin2x cos15x x2 sin2x cos50x dx .$

### 10.1  Program Text

Program Text (d01race.c)

None.

### 10.3  Program Results

Program Results (d01race.r)

## 11  Optional Arguments

This section can be skipped if you wish to use the default values for all optional arguments, otherwise, the following is a list of the optional arguments available. A full description of each optional argument is provided in Section 11.1.
The following optional arguments, see Section 11.2, may be utilized by expert users in conjunction with the information provided in Section 9.1.
• ${\mathbf{Index LDI}}$
• ${\mathbf{Index LDR}}$
• ${\mathbf{Index NSDIV}}$
• ${\mathbf{Index NSEG}}$
• ${\mathbf{Index FCP}}$
• ${\mathbf{Index EVALSP}}$
• ${\mathbf{Index DSP}}$
• ${\mathbf{Index ESP}}$
• ${\mathbf{Index SINFOIP}}$
• ${\mathbf{Index SINFORP}}$

### 11.1  Description of the Optional Arguments

For each option, we give a summary line, a description of the optional argument and details of constraints.
The summary line contains:
• the keywords, where the minimum abbreviation of each keyword is underlined;
• a parameter value, where the letters $a$, $i\text{​ and ​}r$ denote options that take character, integer and real values respectively;
• the default value.
The following symbols represent various machine constants:
All options accept the value ‘DEFAULT’ in order to return single options to their default states.
Keywords and character values are case insensitive, however they must be separated by at least one space.
Unsetable options will return the appropriate value when calling nag_quad_opt_get (d01zlc). They will have no effect if passed to nag_quad_opt_set (d01zkc).
For nag_quad_1d_gen_vec_multi_rcomm (d01rac) the maximum length of the argument cvalue used by nag_quad_opt_get (d01zlc) is $15$.
 Absolute Interval Minimum $r$ Default $\text{}=128.0\epsilon$
$r={\delta }_{a}$, the absolute lower limit for a segment to be considered for subdivision. See also ${\mathbf{Relative Interval Minimum}}$ and Section 9.
Constraint: $r\ge 128\epsilon$.
 Absolute Tolerance $r$ Default $\text{}=1024\epsilon$
$r={\epsilon }_{a}$, the absolute tolerance required. See also ${\mathbf{Relative Tolerance}}$ and Section 3.
Constraint: $r\ge 0.0$.
 Extrapolation $a$ Default $\text{}=\mathrm{ON}$
Activate or deactivate the use of the $\epsilon$ algorithm (Wynn (1956)). ${\mathbf{Extrapolation}}$ often reduces the number of iterations required to achieve the desired solution, but it can occasionally lead to premature convergence towards an incorrect answer.
$\mathrm{ON}$
Use extrapolation.
$\mathrm{OFF}$
Disable extrapolation.
 Extrapolation Safeguard $r$ Default $\text{}=\text{1.0e−12}$
$r={\epsilon }_{\mathit{safe}}$. If ${\epsilon }_{q}$ is the estimated error from the quadrature evaluation alone, and ${\epsilon }_{\mathit{ex}}$ is the error estimate determined using extrapolation, then the extrapolated solution will only be accepted if ${\epsilon }_{\mathit{safe}}{\epsilon }_{q}\le {\epsilon }_{\mathit{ex}}$.
 Maximum Subdivisions $i$ Default $\text{}=50$
$i={\mathrm{max}}_{\mathit{sdiv}}$, the maximum number of subdivisions the algorithm may use in the adaptive phase, forming at most an additional $\left(2×{\mathrm{max}}_{\mathit{sdiv}}\right)$ segments.
 Primary Divisions $i$ Default $\text{}=1$
$i={s}_{\mathit{pri}}$, the number of initial segments of the domain $\left[a,b\right]$. By default the initial segment is the entire domain.
Constraint: $0.
 Primary Division Mode $a$ Default $\text{}='\mathrm{AUTOMATIC}'$
Determines how the initial set of ${s}_{\mathit{pri}}$ segments will be generated.
AUTOMATIC
nag_quad_1d_gen_vec_multi_rcomm (d01rac) will automatically generate ${s}_{\mathit{pri}}$ segments of equal size covering the interval $\left[a,b\right]$.
MANUAL
nag_quad_1d_gen_vec_multi_rcomm (d01rac) will use the break-points ${x}_{\mathit{i}}^{0}$, for $\mathit{i}=1,2,\dots ,{s}_{\mathit{pri}}-1$, supplied in x on initial entry to generate the initial segments covering $\left[a,b\right]$. These may be supplied in any order, however it will be more efficient to supply them in ascending (or descending if $a>b$) order. Repeated break-points are allowed, although this will generate fewer initial segments.
Note: an absolute bound on the size of an initial segment of $10.0\epsilon$ is automatically applied in all cases, and will result in fewer initial subdivisions being generated if automatically generated or supplied break-points result in segments smaller than this..
 Prioritize Error $a$ Default $\text{}=\text{LEVEL}$
Indicates how new subdivisions of segments sustaining unacceptable local errors for integrals should be prioritized.
$\text{LEVEL}$
Segments with lower level with unsatisfactory error estimates will be chosen over segments with greater error on higher levels. This will probably lead to more integrals being improved in earlier iterations of the algorithm, and hence will probably lead to fewer repeated returns (see argument sid), and to more integrals being satisfactorily estimated if computational exhaustion occurs.
$\text{MAXERR}$
The segment with the worst overall error will be split, regardless of level. This will more rapidly improve the worst integral estimates, although it will probably result in the fewest integrals being improved in earlier iterations, and may hence lead to more repeated returns (see argument sid), and potentially fewer integrals satisfying the requested tolerances if computational exhaustion occurs.
 Quadrature Rule $a$ Default $\text{}=\text{GK15}$
The basic quadrature rule to be used during the integration. Currently $6$ Gauss–Kronrod rules are available, all identifiable by the letters GK followed by the number of points required by the Kronrod rule. Higher order rules generally provide higher accuracy with fewer subdivisons. However, for integrands with sharp singularities, lower order rules may be more efficient, particularly if the integrand away from the singularity is well behaved. With higher order rules, you may need to increase the ${\mathbf{Absolute Interval Minimum}}$ and the ${\mathbf{Relative Interval Minimum}}$ to maintain numerical difference between the abscissae and the segment bounds.
GK15
The Gauss–Kronrod rule based on $7$ Gauss points and $15$ Kronrod points.
GK21
The Gauss–Kronrod rule based on $10$ Gauss points and $21$ Kronrod points. This is the rule used by nag_1d_quad_gen_1 (d01sjc).
GK31
The Gauss–Kronrod rule based on $15$ Gauss points and $31$ Kronrod points.
GK41
The Gauss–Kronrod rule based on $20$ Gauss points and $41$ Kronrod points.
GK51
The Gauss–Kronrod rule based on $25$ Gauss points and $51$ Kronrod points.
GK61
The Gauss–Kronrod rule based on $30$ Gauss points and $61$ Kronrod points. This is the highest order rule, most suitable for highly oscilliatory integrals.
 Relative Interval Minimum $r$ Default $\text{}=\text{1.0e−6}$
$r={\delta }_{r}$, the relative factor in the lower limit, ${\delta }_{r}\left|b-a\right|$, for a segment to be considered for subdivision. See also ${\mathbf{Absolute Interval Minimum}}$ and Section 9.
Constraint: $r\ge 0.0$.
 Relative Tolerance $r$ Default $\text{}=\sqrt{\epsilon }$
$r={\epsilon }_{r}$, the required relative tolerance. See also ${\mathbf{Absolute Tolerance}}$ and Section 3.
Constraint: $r\ge 0.0$.
Note: setting both ${\epsilon }_{r}={\epsilon }_{a}=0.0$ is possible, although it will most likely result in an excessive amount of computational effort.

### 11.2  Diagnostic Options

These options are provided for expert users who wish to examine and modify the precise details of the computation. They should only be used after nag_quad_1d_gen_vec_multi_rcomm (d01rac) returns, as opposed to the options listed in Section 11.1 which must be used before the first call to nag_quad_1d_gen_vec_multi_rcomm (d01rac).
 Index LDI $i$ query only
${I}_{\mathit{ldi}}$, the index of icom required for obtaining $\mathit{ldi}$. See Section 9.1.
 Index LDR $i$ query only
${I}_{\mathit{ldr}}$, the index of icom required for obtaining $\mathit{ldr}$. See Section 9.1.
 Index NSDIV $i$ query only
${I}_{\mathit{nsdiv}}$, the index of icom required for obtaining $\mathit{nsdiv}$. See Section 9.1.
 Index NSEG $i$ query only
${I}_{\mathit{nseg}}$, the index of icom required for obtaining $\mathit{nseg}$. See Section 9.1.
 Index FCP $i$ query only
${I}_{\mathit{fcp}}$, the index of icom required for obtaining $\mathit{fcp}$. See Section 9.1.
 Index EVALSP $i$ query only
${I}_{\mathit{evalsp}}$, the index of icom required for obtaining $\mathit{evalsp}$. See Section 9.1.
 Index DSP $i$ query only
${I}_{\mathit{dsp}}$, the index of icom required for obtaining $\mathit{dsp}$. See Section 9.1.
 Index ESP $i$ query only
${I}_{\mathit{esp}}$, the index of icom required for obtaining $\mathit{esp}$. See Section 9.1.
 Index SINFOIP $i$ query only
${I}_{\mathit{sinfoip}}$, the index of icom required for obtaining $\mathit{sinfoip}$. See Section 9.1.
 Index SINFORP $i$ query only
${I}_{\mathit{sinforp}}$, the index of icom required for obtaining $\mathit{sinforp}$. See Section 9.1.