d01 Chapter Contents
NAG C Library Manual

# NAG Library Chapter Introductiond01 – Quadrature

## 1  Scope of the Chapter

This chapter provides functions for the numerical evaluation of definite integrals in one or more dimensions.

## 2  Background to the Problems

The functions in this chapter are designed to estimate:
(a) the value of a one-dimensional definite integral of the form
 $∫abfxdx$ (1)
where $f\left(x\right)$ is defined by you, either at a set of points $\left({x}_{\mathit{i}},f\left({x}_{\mathit{i}}\right)\right)$, for $\mathit{i}=1,2,\dots ,n$, where $a={x}_{1}<{x}_{2}<\cdots <{x}_{n}=b$, or in the form of a function; and the limits of integration $a,b$ may be finite or infinite.
Some methods are specially designed for integrands of the form
 $fx=wxgx$ (2)
which contain a factor $w\left(x\right)$, called the weight-function, of a specific form. These methods take full account of any peculiar behaviour attributable to the $w\left(x\right)$ factor.
(b) the values of the one-dimensional indefinite integrals arising from (1) where the ranges of integration are interior to the interval $\left[a,b\right]$.
(c) the value of a multidimensional definite integral of the form
 $∫Rnfx1,x2,…,xndxn⋯dx2dx1$ (3)
where $f\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)$ is a function defined by you and ${R}_{n}$ is some region of $n$-dimensional space.
The simplest form of ${R}_{n}$ is the $n$-rectangle defined by
 $ai≤xi≤bi, i=1,2,…,n$ (4)
where ${a}_{i}$ and ${b}_{i}$ are constants. When ${a}_{i}$ and ${b}_{i}$ are functions of ${x}_{j}$ ($j), the region can easily be transformed to the rectangular form (see page 266 of Davis and Rabinowitz (1975)). Some of the methods described incorporate the transformation procedure.

### 2.1  One-dimensional Integrals

To estimate the value of a one-dimensional integral, a quadrature rule uses an approximation in the form of a weighted sum of integrand values, i.e.,
 $∫abfxdx≃∑i=1Nwifxi.$ (5)
The points ${x}_{i}$ within the interval $\left[a,b\right]$ are known as the abscissae, and the ${w}_{i}$ are known as the weights.
More generally, if the integrand has the form (2), the corresponding formula is
 $∫abwxgxdx≃∑i=1Nwigxi.$ (6)
If the integrand is known only at a fixed set of points, these points must be used as the abscissae, and the weighted sum is calculated using finite difference methods. However, if the functional form of the integrand is known, so that its value at any abscissa is easily obtained, then a wide variety of quadrature rules are available, each characterised by its choice of abscissae and the corresponding weights.
The appropriate rule to use will depend on the interval $\left[a,b\right]$ – whether finite or otherwise – and on the form of any $w\left(x\right)$ factor in the integrand. A suitable value of $N$ depends on the general behaviour of $f\left(x\right)$; or of $g\left(x\right)$, if there is a $w\left(x\right)$ factor present.
Among possible rules, we mention particularly the Gaussian formulae, which employ a distribution of abscissae which is optimal for $f\left(x\right)$ or $g\left(x\right)$ of polynomial form.
The choice of basic rules constitutes one of the principles on which methods for one-dimensional integrals may be classified. The other major basis of classification is the implementation strategy, of which some types are now presented.
(a) Single rule evaluation procedures
A fixed number of abscissae, $N$, is used. This number and the particular rule chosen uniquely determine the weights and abscissae. No estimate is made of the accuracy of the result.
(b) Automatic procedures
The number of abscissae, $N$, within $\left[a,b\right]$ is gradually increased until consistency is achieved to within a level of accuracy (absolute or relative) requested by you. There are essentially two ways of doing this; hybrid forms of these two methods are also possible:
 (i) whole interval procedures (non-adaptive) A series of rules using increasing values of $N$ are successively applied over the whole interval $\left[a,b\right]$. It is clearly more economical if abscissae already used for a lower value of $N$ can be used again as part of a higher-order formula. This principle is known as optimal extension. There is no overlap between the abscissae used in Gaussian formulae of different orders. However, the Kronrod formulae are designed to give an optimal $\left(2N+1\right)$-point formula by adding $\left(N+1\right)$ points to an $N$-point Gauss formula. Further extensions have been developed by Patterson. (ii) adaptive procedures The interval $\left[a,b\right]$ is repeatedly divided into a number of sub-intervals, and integration rules are applied separately to each sub-interval. Typically, the subdivision process will be carried further in the neighbourhood of a sharp peak in the integrand than where the curve is smooth. Thus, the distribution of abscissae is adapted to the shape of the integrand. Subdivision raises the problem of what constitutes an acceptable accuracy in each sub-interval. The usual global acceptability criterion demands that the sum of the absolute values of the error estimates in the sub-intervals should meet the conditions required of the error over the whole interval. Automatic extrapolation over several levels of subdivision may eliminate the effects of some types of singularities.
An ideal general-purpose method would be an automatic method which could be used for a wide variety of integrands, was efficient (i.e., required the use of as few abscissae as possible), and was reliable (i.e., always gave results to within the requested accuracy). Complete reliability is unobtainable, and generally higher reliability is obtained at the expense of efficiency, and vice versa. It must therefore be emphasized that the automatic functions in this chapter cannot be assumed to be $100%$ reliable. In general, however, the reliability is very high.

### 2.2  Multidimensional Integrals

A distinction must be made between cases of moderately low dimensionality (say, up to $4$ or $5$ dimensions), and those of higher dimensionality. Where the number of dimensions is limited, a one-dimensional method may be applied to each dimension, according to some suitable strategy, and high accuracy may be obtainable (using product rules). However, the number of integrand evaluations rises very rapidly with the number of dimensions, so that the accuracy obtainable with an acceptable amount of computational labour is limited; for example a product of $3$-point rules in $20$ dimensions would require more than ${10}^{9}$ integrand evaluations. Special techniques such as the Monte–Carlo methods can be used to deal with high dimensions.
(a) Products of one-dimensional rules
Using a two-dimensional integral as an example, we have
 $∫a1b1∫a2b2fx,ydy dx≃∑i=1Nwi ∫a2b2fxi,ydy$ (7)
 $∫a1b1∫a2b2fx,ydy dx≃∑i=1N∑j=1Nwivjfxi,yj$ (8)
where $\left({w}_{i},{x}_{i}\right)$ and $\left({v}_{i},{y}_{i}\right)$ are the weights and abscissae of the rules used in the respective dimensions.
A different one-dimensional rule may be used for each dimension, as appropriate to the range and any weight function present, and a different strategy may be used, as appropriate to the integrand behaviour as a function of each independent variable.
For a rule-evaluation strategy in all dimensions, the formula (8) is applied in a straightforward manner. For automatic strategies (i.e., attempting to attain a requested accuracy), there is a problem in deciding what accuracy must be requested in the inner integral(s). Reference to formula (7) shows that the presence of a limited but random error in the $y$-integration for different values of ${x}_{i}$ can produce a ‘jagged’ function of $x$, which may be difficult to integrate to the desired accuracy and for this reason products of automatic one-dimensional functions should be used with caution (see Lyness (1983)).
(b) Monte–Carlo methods
These are based on estimating the mean value of the integrand sampled at points chosen from an appropriate statistical distribution function. Usually a variance reducing procedure is incorporated to combat the fundamentally slow rate of convergence of the rudimentary form of the technique. These methods can be effective by comparison with alternative methods when the integrand contains singularities or is erratic in some way, but they are of quite limited accuracy.
An automatic adaptive strategy in several dimensions normally involves division of the region into subregions, concentrating the divisions in those parts of the region where the integrand is worst behaved. It is difficult to arrange with any generality for variable limits in the inner integral(s). For this reason, some methods use a region where all the limits are constants; this is called a hyper-rectangle. Integrals over regions defined by variable or infinite limits may be handled by transformation to a hyper-rectangle. Integrals over regions so irregular that such a transformation is not feasible may be handled by surrounding the region by an appropriate hyper-rectangle and defining the integrand to be zero outside the desired region. Such a technique should always be followed by a Monte–Carlo method for integration.
The method used locally in each subregion produced by the adaptive subdivision process is usually one of three types: Monte–Carlo, number theoretic or deterministic. Deterministic methods are usually the most rapidly convergent but are often expensive to use for high dimensionality and not as robust as the other techniques.

## 3  Recommendations on Choice and Use of Available Functions

The following three sub-sections consider in turn functions for: one-dimensional integrals over a finite interval, and over a semi-infinite or an infinite interval; and multidimensional integrals. Within each sub-section, functions are classified by the type of method, which ranges from simple rule evaluation to automatic adaptive algorithms. The recommendations apply particularly when the primary objective is simply to compute the value of one or more integrals, and in these cases the automatic adaptive functions are generally the most convenient and reliable, although also the most expensive in computing time.
Note however that in some circumstances it may be counter-productive to use an automatic function. If the results of the quadrature are to be used in turn as input to a further computation (e.g., an ‘outer’ quadrature or an optimization problem), then this further computation may be adversely affected by the ‘jagged performance profile’ of an automatic function; a simple rule-evaluation function may provide much better overall performance. For further guidance, the article by Lyness (1983) is recommended.

### 3.1  One-dimensional Integrals over a Finite Interval

(a) Integrand defined at a set of points
If $f\left(x\right)$ is defined numerically at four or more points, then the Gill–Miller finite difference method (nag_1d_quad_vals (d01gac)) should be used. The interval of integration is taken to coincide with the range of $x$ values of the points supplied. It is in the nature of this problem that any function may be unreliable. In order to check results independently and so as to provide an alternative technique you may fit the integrand by Chebyshev series using nag_1d_cheb_fit (e02adc) and then use functions nag_1d_cheb_intg (e02ajc) and nag_1d_cheb_eval2 (e02akc) to evaluate its integral (which need not be restricted to the range of the integration points, as is the case for nag_1d_quad_vals (d01gac)). A further alternative is to fit a cubic spline to the data using nag_1d_spline_fit_knots (e02bac) and then to evaluate its integral using nag_1d_spline_intg (e02bdc).
(b) Integrand defined as a function
If the functional form of $f\left(x\right)$ is known, then one of the following approaches should be taken. They are arranged in the order from most specific to most general, hence the first applicable procedure in the list will be the most efficient. However, if you do not wish to make any assumptions about the integrand, the most reliable functions to use will be nag_1d_quad_gen_1 (d01sjc).
(i) Rule-evaluation functions
If $f\left(x\right)$ is known to be sufficiently well behaved (more precisely, can be closely approximated by a polynomial of moderate degree), a Gaussian function with a suitable number of abscissae may be used.
nag_quad_1d_gauss_wset (d01tbc) or nag_quad_1d_gauss_wgen (d01tcc) with nag_quad_md_gauss (d01fbc) may be used if it is required to examine the weights and abscissae.
nag_quad_1d_gauss_wset (d01tbc) is faster and more accurate, whereas nag_quad_1d_gauss_wgen (d01tcc) is more general.
If $f\left(x\right)$ is well behaved, apart from a weight-function of the form
 $x-a+b2 c or b-xcx-ad,$
Firstly, several functions are available for integrands of the form $w\left(x\right)g\left(x\right)$ where $g\left(x\right)$ is a ‘smooth’ function (i.e., has no singularities, sharp peaks or violent oscillations in the interval of integration) and $w\left(x\right)$ is a weight function of one of the following forms.
1. if $w\left(x\right)={\left(b-x\right)}^{\alpha }{\left(x-a\right)}^{\beta }{\left(\mathrm{log}\left(b-x\right)\right)}^{k}{\left(\mathrm{log}\left(x-a\right)\right)}^{l}$, where $k,l=0\text{​ or ​}1$, $\alpha ,\beta >-1$: use nag_1d_quad_wt_alglog_1 (d01spc);
2. if $w\left(x\right)=\frac{1}{x-c}$: use nag_1d_quad_wt_cauchy_1 (d01sqc) (this integral is called the Hilbert transform of $g$);
3. if $w\left(x\right)=\mathrm{cos}\left(\omega x\right)$ or $\mathrm{sin}\left(\omega x\right)$: use nag_1d_quad_wt_trig_1 (d01snc) (this function can also handle certain types of singularities in $g\left(x\right)$).
Secondly, there are some functions for general $f\left(x\right)$. If $f\left(x\right)$ is known to be free of singularities, though it may be oscillatory, nag_1d_quad_osc_1 (d01skc) may be used.
The most powerful of the finite interval integration functions are nag_1d_quad_gen_1 (d01sjc) and nag_1d_quad_brkpts_1 (d01slc), which can cope with singularities of several types. They may be used if none of the more specific situations described above applies and are somewhat more reliable, particularly where the integrand has singularities other than at an end point, or has discontinuities or cusps, and are therefore recommended where the integrand is known to be badly behaved. nag_1d_quad_brkpts_1 (d01slc) is recommended when the locations of singularities are known while nag_1d_quad_gen_1 (d01sjc) is recommended when the nature of the integrand is completely unknown.
If $f\left(x\right)$ has singularities of certain types, discontinuities (including switches in definition) or sharp peaks occurring at known points, the integral should be evaluated separately over each of the subranges or nag_1d_quad_brkpts_1 (d01slc) may be used.

### 3.2  One-dimensional Integrals over a Semi-infinite or Infinite Interval

(a) Integrand defined at a set of points
If $f\left(x\right)$ is defined numerically at four or more points, and the portion of the integral lying outside the range of the points supplied may be neglected, then the Gill–Miller finite difference method, nag_1d_quad_vals (d01gac), should be used.
(b) Integrand defined as a function
(i) Rule evaluation functions
If $f\left(x\right)$ behaves approximately like a polynomial in $x$, apart from a weight function of the form:
1. ${e}^{-\beta x},\beta >0$ (semi-infinite interval, lower limit finite); or
2. ${e}^{-\beta x},\beta <0$ (semi-infinite interval, upper limit finite); or
3. ${e}^{-\beta {\left(x-\alpha \right)}^{2}},\beta >0$ (infinite interval),
or if $f\left(x\right)$ behaves approximately like a polynomial in ${\left(x+b\right)}^{-1}$ (semi-infinite range), then the Gaussian functions may be used.
nag_1d_quad_gauss_1 (d01tac) may be used if it is not required to examine the weights and abscissae.
nag_quad_1d_gauss_wset (d01tbc) or nag_quad_1d_gauss_wgen (d01tcc) with nag_quad_md_gauss (d01fbc) may be used if it is required to examine the weights and abscissae.
nag_quad_1d_gauss_wset (d01tbc) is faster and more accurate, whereas nag_quad_1d_gauss_wgen (d01tcc) is more general.
nag_1d_quad_inf_1 (d01smc) may be used, except for integrands which decay slowly towards an infinite end point, and oscillate in sign over the entire range. For this class, it may be possible to calculate the integral by integrating between the zeros and invoking some extrapolation process.
nag_1d_quad_inf_wt_trig_1 (d01ssc) may be used for integrals involving weight functions of the form $\mathrm{cos}\left(\omega x\right)$ and $\mathrm{sin}\left(\omega x\right)$ over a semi-infinite interval (lower limit finite).
The following alternative procedures are mentioned for completeness, though their use will rarely be necessary.
1. If the integrand decays rapidly towards an infinite end point, a finite cut-off may be chosen, and the finite range methods applied.
2. If the only irregularities occur in the finite part (apart from a singularity at the finite limit, with which nag_1d_quad_inf_1 (d01smc) can cope), the range may be divided, with nag_1d_quad_inf_1 (d01smc) used on the infinite part.
3. A transformation to finite range may be employed,e.g.,
 $x=1-tt or x=-loge⁡t$
will transform $\left(0,\infty \right)$ to $\left(1,0\right)$ while for infinite ranges we have
 $∫-∞∞fxdx=∫0∞fx+f-xdx.$
If the integrand behaves badly on $\left(-\infty ,0\right)$ and well on $\left(0,\infty \right)$ or vice versa it is better to compute it as $\underset{-\infty }{\overset{0}{\int }}f\left(x\right)dx+\underset{0}{\overset{\infty }{\int }}f\left(x\right)dx$. This saves computing unnecessary function values in the semi-infinite range where the function is well behaved.

### 3.3  Multidimensional Integrals

(a) Products of one-dimensional rules (suitable for up to about $5$ dimensions)
If $f\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)$ is known to be a sufficiently well behaved function of each variable ${x}_{i}$, apart possibly from weight functions of the types provided, a product of Gaussian rules may be used. These are provided by nag_quad_1d_gauss_wset (d01tbc) or nag_quad_1d_gauss_wgen (d01tcc) with nag_quad_md_gauss (d01fbc). Rules for finite, semi-infinite and infinite ranges are included.
For two-dimensional integrals only, unless the integrand is very badly behaved, the automatic whole-interval product procedure of nag_quad_2d_fin (d01dac) may be used. The limits of the inner integral may be user-specified functions of the outer variable. Infinite limits may be handled by transformation (see Section 3.2); end point singularities introduced by transformation should not be troublesome, as the integrand value will not be required on the boundary of the region.
If none of these functions proves suitable and convenient, the one-dimensional functions may be used recursively. For example, the two-dimensional integral
 $I=∫a1b1∫a2b2fx,ydy dx$
may be expressed as
 $I=∫a1b1 Fxdx, where Fx=∫a2b2 fx,ydy.$
The user-supplied code to evaluate $F\left(x\right)$ will call the integration function for the $y$-integration, which will call more user-supplied code for $f\left(x,y\right)$ as a function of $y$ ($x$ being effectively a constant). Note that, for reasons of efficiency and robustness the integration functions are not defined as recursive, and thus a different library integration function must be used for each dimension. Apart from this restriction, the following combinations are not permitted: nag_1d_quad_gen_1 (d01sjc) and nag_1d_quad_brkpts_1 (d01slc), nag_1d_quad_wt_trig_1 (d01snc) and nag_1d_quad_wt_alglog_1 (d01spc), nag_1d_quad_wt_alglog_1 (d01spc) and nag_1d_quad_wt_cauchy_1 (d01sqc), nag_1d_quad_wt_trig_1 (d01snc) and nag_1d_quad_wt_cauchy_1 (d01sqc), nag_1d_quad_wt_trig_1 (d01snc) and nag_1d_quad_inf_wt_trig_1 (d01ssc), nag_1d_quad_inf_1 (d01smc) and nag_1d_quad_inf_wt_trig_1 (d01ssc), nag_1d_quad_gen_1 (d01sjc) and nag_1d_quad_osc_1 (d01skc). Otherwise the full range of one-dimensional functions are available, for finite/infinite intervals, constant/variable limits, rule evaluation/automatic strategies etc.
(b) Sag–Szekeres method
nag_quad_md_sphere (d01fdc) is particularly suitable for integrals of very large dimension although the accuracy is generally not high. It allows integration over either the general product region (with built-in transformation to the $n$-cube) or the $n$-sphere. Although no error estimate is provided, two adjustable arguments may be varied for checking purposes or may be used to tune the algorithm to particular integrals.
(c) A combinatorial extrapolation method
nag_quad_md_simplex (d01pac) computes a sequence of approximations and an error estimate to the integral of a function over a multidimensional simplex using a combinatorial method with extrapolation.
Both functions are for integrals of the form
 $∫a1b1 ∫a2b2 ⋯ ∫anbn fx1,x2,…,xndxndxn-1⋯dx1.$
nag_multid_quad_monte_carlo_1 (d01xbc) is an adaptive Monte–Carlo function. This function is usually slow and not recommended for high-accuracy work. It is a robust function that can often be used for low-accuracy results with highly irregular integrands or when $n$ is large.
nag_multid_quad_adapt_1 (d01wcc) is an adaptive deterministic function. Convergence is fast for well behaved integrands. Highly accurate results can often be obtained for $n$ between $2$ and $5$, using significantly fewer integrand evaluations than would be required by nag_multid_quad_monte_carlo_1 (d01xbc). The function will usually work when the integrand is mildly singular and for $n\le 10$ should be used before nag_multid_quad_monte_carlo_1 (d01xbc). If it is known in advance that the integrand is highly irregular, it is best to compare results from at least two different functions.
There are many problems for which one or both of the functions will require large amounts of computing time to obtain even moderately accurate results. The amount of computing time is controlled by the number of integrand evaluations allowed by you, and you should set this argument carefully, with reference to the time available and the accuracy desired.

## 4  Decision Trees

### Tree 1: One-dimensional integrals over a finite interval

 Is the functional form of the integrand known? _yes Are you concerned with efficiency for simple integrals? _yes Is the integrand smooth (polynomial-like) apart from weight function ${\left|x-\left(a+b\right)/2\right|}^{c}$ or ${\left(b-x\right)}^{c}{\left(x-a\right)}^{d}$? _yes d01tac, d01tbc or d01tcc and d01fbc or d01gdc | | no| | | Is the integrand reasonably smooth and the required accuracy not too great? _yes d01bdc | | no| | | Has the integrand discontinuities, sharp peaks or singularities at known points other than the end points? _yes Split the range and begin again; or use d01slc | | no| | | Is the integrand free of singularities, sharp peaks and violent oscillations apart from weight function ${\left(b-x\right)}^{\alpha }{\left(x-a\right)}^{\beta }\phantom{\rule{0ex}{0ex}}{\left(\mathrm{log}\left(b-x\right)\right)}^{k}{\left(\mathrm{log}\left(x-a\right)\right)}^{l}$? _yes d01spc | | no| | | Is the integrand free of singularities, sharp peaks and violent oscillations apart from weight function $\frac{1}{x-c}$? _yes d01sqc | | no| | | Is the integrand free of violent oscillations apart from weight function $\mathrm{cos}\left(\omega x\right)$ or $\mathrm{sin}\left(\omega x\right)$? _yes d01snc | | no| | | Is the integrand free of singularities? _yes d01skc | | no| | | d01sjc | no| | d01sjc no| d01gac

### Tree 2: One-dimensional integrals over a semi-infinite or infinite interval

 Is the functional form of the integrand known? _yes Are you concerned with efficiency for simple integrands? _yes Is the integrand smooth (polynomial-like) with no exceptions? _yes d01bdc | | no| | | Is the integrand smooth (polynomial-like) apart from weight function ${e}^{-\beta \left(x\right)}$ (semi-infinite range) or ${e}^{{-\beta \left(x-a\right)}^{2}}$ (infinite range) or is the integrand polynomial-like in $\frac{1}{x+b}$? (semi-infinite range)? _yes d01tac, d01tbc and d01fbc or d01tcc and d01fbc | | no| | | Has integrand discontinuities, sharp peaks or singularities at known points other than a finite limit? _yes Split range; begin again using finite or infinite range trees | | no| | | Does the integrand oscillate over the entire range? _yes Does the integrand decay rapidly towards an infinite limit? _yes Use d01smc; or set cutoff and use finite range tree | | | no| | | | Is the integrand free of violent oscillations apart from weight function $\mathrm{cos}\left(\omega x\right)$ or $\mathrm{sin}\left(\omega x\right)$ (semi-infinite range)? _yes d01ssc | | | no| | | | Use finite-range integration between the zeros and extrapolate. | | no| | | d01smc | no| | d01smc no| d01gac (integrates over the range of the points supplied)

### Tree 3: Multidimensional integrals

 Is dimension $\text{}=2$ and product region? _yes d01dac no| Is dimension $\text{}\le 4$ _yes Is region an $n$-sphere? _yes d01fbc with user transformation. | no| | Is region a Simplex? _yes d01fbc with user transformation or d01pac | no| | Is the integrand smooth (polynomial-like) in each dimension apart from weight function? _yes d01tbc and d01fbc or d01tcc and d01fbc | no| | Is integrand free of extremely bad behaviour? _yes d01fdc, d01gdc or d01wcc | no| | Is bad behaviour on the boundary? _yes d01fdc or d01wcc | no| | Compare results from at least two of d01fdc, d01gdc, d01wcc and d01xbc and one-dimensional recursive application no| Is region an $n$-sphere? _yes d01fdc no| Is region a Simplex? _yes d01pac no| Is high accuracy required? _yes d01fdc with argument tuning no| Is dimension high? _yes d01fdc or d01gdc no| d01wcc

## 5  Functionality Index

 Korobov optimal coefficients for use in nag_quad_md_numth_vec (d01gdc):
 when number of points is a product of 2 primes nag_quad_md_numth_coeff_2prime (d01gzc)
 when number of points is prime nag_quad_md_numth_coeff_prime (d01gyc)
 over a general product region:
 Sag–Szekeres method (also over n-sphere) nag_quad_md_sphere (d01fdc)
 over a hyper-rectangle:
 adaptive integration of a function over a finite interval:
 strategy due to Piessens and de Doncker,
 weight function 1 / (x − c) Cauchy principal value (Hilbert transform), thread-safe nag_1d_quad_wt_cauchy_1 (d01sqc)
 adaptive integration of a function over an infinite interval or semi-infinite interval:
 integration of a function defined by data values only,
 Weights and abscissae for Gaussian quadrature rules:
 more general choice of rule,
 calculating the weights and abscissae nag_quad_1d_gauss_wgen (d01tcc)
 restricted choice of rule,
 using pre-computed weights and abscissae nag_quad_1d_gauss_wset (d01tbc)