Hjorth-Jensen M. Computational Physics
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8.3. GAUSSIAN QUADRATURE 119
and
(8.72)
They fulfil the orthorgonality relation
(8.73)
and the recursion relation
(8.74)
Hermite polynomials
In a similar way, for an integral which goes like
(8.75)
we could use the Hermite polynomials in order to extract weights and mesh points. The Hermite
polynomials are the solutions of the following differential equation
(8.76)
A typical example is again the solution of Schrödinger’s equation, but this time with a harmonic
oscillator potential. The first few polynomials are
(8.77)
(8.78)
(8.79)
(8.80)
and
(8.81)
They fulfil the orthorgonality relation
(8.82)
and the recursion relation
(8.83)
120 CHAPTER 8. NUMERICAL INTEGRATION
Table 8.1: Mesh points and weights for the integration interval [0,100] with using the
Gauss-Legendre method.
1 1.305 3.334
2 6.747 7.473
3 16.030 10.954
4 28.330 13.463
5 42.556 14.776
6 57.444 14.776
7 71.670 13.463
8 83.970 10.954
9 93.253 7.473
10 98.695 3.334
8.3.6 Applications to selected integrals
Before we proceed with some selected applications, it is important to keep in mind that since
the mesh points are not evenly distributed, a careful analysis of the behavior of the integrand as
function of
and the location of mesh points is mandatory. To give you an example, in the Table
below we show the mesh points and weights for the integration interval [0,100] for
points obtained by the Gauss-Legendre method. Clearly, if your function oscillates strongly in
any subinterval, this approach needs to be refined, either by choosing more points or by choosing
other integration methods. Note also that for integration intervals like e.g.,
, the
Gauss-Legendre method places more points at the beginning of the integration interval. If your
integrand varies slowly for large values of , then this method may be appropriate.
Let us here compare three methods for integrating, namely the trapezoidal rule, Simpson’s
method and the Gauss-Legendre approach. We choose two functions to integrate, namely
and
A program example which uses the trapezoidal rule, Simpson’s rule and the Gauss-Legendre
method is included here.
#include < iostream >
#include
using namespace std ;
/ / Here we define various f un c ti ons called by the main program
/ / t h i s func tion d ef in es the f un ct io n to i nteg r at e
double int_ f un ctio n ( double x ) ;
8.3. GAUSSIAN QUADRATURE 121
/ / Main function begins here
int main ()
{
int n ;
double a , b ;
cout < < < < endl ;
cin > > n ;
cout < < < < endl ;
cin > > a > > b ;
/ / reserve space in memory for ve ctors containing the mesh points
/ / weights and fu nc ti on values for the use of the gauss legendre
/ / method
double x = new double [ n ] ;
double w = new double [ n ] ;
/ / s et up the mesh points and weights
gauleg ( a , b , x ,w, n ) ;
/ / evaluate the i n t egr a l with the Gauss Legendre method
double int _gauss = 0 . ;
for ( int i = 0 ; i < n ; i ++){
int _gauss +=w[ i ] i nt_ f un c tio n ( x [ i ]) ;
}
/ / f i n a l output
cout < < < < t r a pezo i d al_r u le ( a , b , n ,
i nt_ fun c ti o n )
<< endl ;
cout < < < < simpson ( a , b , n , i n t_f unc t io n )
<< endl ;
cout < < < < int _gauss < < endl ;
delete [ ] x ;
delete [ ] w;
return 0 ;
} / / end of main program
/ / t h i s f un ct ion d ef ines the fu nc ti on to i nte g rate
double int_ f un ctio n ( double x )
{
double value = 4 . / ( 1 . + x x ) ;
return value ;
} / / end of func tion to evaluate
In Table 8.2 we show the results for the first integral using various mesh points, while Table
8.3 displays the corresponding results obtained with the second integral. We note here that, since
the area over where we integrate is rather large and the integrand goes slowly to zero for large
values of
, both the trapezoidal rule and Simpson’s method need quite many points in order to
approach the Gauss-Legendre method. This integrand demonstrates clearly the strength of the
Gauss-Legendre method (and other GQ methods as well), viz., few points are needed in order to
122 CHAPTER 8. NUMERICAL INTEGRATION
Table 8.2: Results for using three different methods as functions of the num-
ber of mesh points
.
Trapez Simpson Gauss-Legendre
10 1.821020 1.214025 0.1460448
20 0.912678 0.609897 0.2178091
40 0.478456 0.333714 0.2193834
100 0.273724 0.231290 0.2193839
1000 0.219984 0.219387 0.2193839
achieve a very high precision.
The second Table however shows that for smaller integration intervals, both the trapezoidal
rule and Simpson’s method compare well with the results obtained with the Gauss-Legendre
approach.
Table 8.3: Results for
using three different methods as functions of the number
of mesh points
.
Trapez Simpson Gauss-Legendre
10 0.798861 0.799231 0.799233
20 0.799140 0.799233 0.799233
40 0.799209 0.799233 0.799233
100 0.799229 0.799233 0.799233
1000 0.799233 0.799233 0.799233
8.4 Treatment of singular Integrals
So-called principal value (PV) integrals are often employed in physics, from Green’s functions
for scattering to dispersion relations. Dispersion relations are often related to measurable quan-
tities and provide important consistency checks in atomic, nuclear and particle physics. A PV
integral is defined as
(8.84)
and arises in applications of Cauchy’s residue theorem when the pole
lies on the real axis
within the interval of integration .
An important assumptionis that the function
is continuous on the interval of integration.
In case is an analytic expression or it has an analytic continuation in the complex plane,
it may be possible to obtain an expression on closed form for the above integral.
8.4. TREATMENT OF SINGULAR INTEGRALS 123
However, the situation which we are often confronted with is that is only known at some
points
with corresponding values . In order to obtain we need to resort to a numerical
evaluation.
To evaluate such an integral, let us first rewrite it as
(8.85)
where we have isolated the principal value part in the last integral.
Defining a new variable
, we can rewrite the principal value integral as
(8.86)
One possibility is to Taylor expand around , and compute derivatives to a certain
order as we did for the Trapezoidal rule or Simpson’s rule. Since all terms with even powers of
in the Taylor expansion dissapear, we have that
(8.87)
To evaluate higher-order derivativesmay be both time consuming and delicate from a numer-
ical point of view, since there is always the risk of loosing precision when calculating derivatives
numerically. Unless we have an analytic expression for
and can evaluate the derivatives
in a closed form, the above approach is not the preferred one.
Rather, we show here how to use the Gauss-Legendre method to compute Eq. (8.86). Let us
first introduce a new variable
and rewrite Eq. (8.86) as
(8.88)
The integration limits are now from
to , as for the Legendre polynomials. The principal
value in Eq. (8.88) is however rather tricky to evaluate numerically, mainly since computers have
limited precision. We will here use a subtraction trick often used when dealing with singular
integrals in numerical calculations. We introduce first the calculus relation
(8.89)
It means that the curve has equal and opposite areas on both sides of the singular point
.
If we then note that is just a constant, we have also
(8.90)
124 CHAPTER 8. NUMERICAL INTEGRATION
Subtracting this equation from Eq. (8.88) yields
(8.91)
and the integrand is now longer singular since we have that
and
for the particular case the integrand is now finite.
Eq. (8.91) is now rewritten using the Gauss-Legendre method resulting in
(8.92)
where are the mesh points ( in total) and are the weights.
In the selection of mesh points for a PV integral, it is important to use an even number of
points, since an odd number of mesh points always picks
as one of the mesh points. The
sum in Eq. (8.92) will then diverge.
Let us apply this method to the integral
(8.93)
The integrand diverges at
. We rewrite it using Eq. (8.91) as
(8.94)
since
. With Eq. (8.92) we have then
(8.95)
The exact results is
. With just two mesh points we recall from the previous
subsection that and that the mesh points are the zeros of , namely
and . Setting and inserting these values in the last equation gives
With six mesh points we get even the exact result to the tenth digit
We can repeat the above subtraction trick for more complicated integrands. First we modify
the integration limits to and use the fact that
(8.96)
8.4. TREATMENT OF SINGULAR INTEGRALS 125
It means that the curve has equal and opposite areas on both sides of the singular
point
. If we break the integral into one over positive and one over negative , a change of
variable allows us to rewrite the last equation as
(8.97)
We can use this to express a principal values integral as
(8.98)
where the right-hand side is no longer singular at , it is proportional to the derivative
, and can be evaluated numerically as any other integral.
Such a trick is often used when evaluating scattering equations in momentum space, which
are nothing but mere rewriting, for the non-relativistic case, of the Schrödinger equation from
coordinate space to momentum space. We are going to solve numerically the scattering equation
in momentum space in the chapter on eigenvalue equations, see Chapter 13.
Chapter 9
Outline of the Monte-Carlo strategy
9.1 Introduction
Monte Carlo methods are widely used, from the integration of multi-dimensional integrals to
problems in chemistry, physics, medicine, biology, or Dow-Jones forecasting!
Numerical methods that are known as Monte Carlo methods can be loosely described as
statistical simulation methods, where statistical simulation is defined in quite general terms to be
any method that utilizes sequences of random numbers to perform the simulation.
Statistical simulation methods may be contrasted to conventional numerical discretization
methods, which typically are applied to ordinary or partial differential equations that describe
some underlying physical or mathematical system. In many applications of Monte Carlo, the
physical process is simulated directly, and there is no need to even write down the differential
equations that describe the behavior of the system. The only requirement is that the physical
(or mathematical) system be described by probability distribution functions (PDF’s). Once the
PDF’s are known, the Monte Carlo simulation can proceed by random sampling from the PDF’s.
Many simulations are then performed (multiple “trials” or “histories”) and the desired result is
taken as an average over the number of observations (which may be a single observation or
perhaps millions of observations). In many practical applications, one can predict the statistical
error (the “variance”) in this average result, and hence an estimate of the number of Monte Carlo
trials that are needed to achieve a given error. If we assume that the physical system can be
described by a given probability density function, then the Monte Carlo simulation can proceed
by sampling from these PDF’s, which necessitates a fast and effective way to generate random
numbers uniformly distributed on the interval [0,1]. The outcomes of these random samplings,
or trials, must be accumulated or tallied in an appropriate manner to produce the desired result,
but the essential characteristic of Monte Carlo is the use of random sampling techniques (and
perhaps other algebra to manipulate the outcomes) to arrive at a solution of the physical problem.
In contrast, a conventional numerical solution approach would start with the mathematical model
of the physical system, discretizing the differential equations and then solving a set of algebraic
equations for the unknown state of the system. It should be kept in mind though that this general
description of Monte Carlo methods may not directly apply to some applications. It is natural
127
128 CHAPTER 9. OUTLINE OF THE MONTE-CARLO STRATEGY
to think that Monte Carlo methods are used to simulate random, or stochastic, processes, since
these can be described by PDF’s. However, this coupling is actually too restrictive because
many Monte Carlo applications have no apparent stochastic content, such as the evaluation of
a definite integral or the inversion of a system of linear equations. However, in these cases and
others, one can pose the desired solution in terms of PDF’s, and while this transformation may
seem artificial, this step allows the system to be treated as a stochastic process for the purpose of
simulation and hence Monte Carlo methods can be applied to simulate the system.
There are, at least four ingredients which are crucial in order to understand the basic Monte-
Carlo strategy. These are
1. Random variables,
2. probability distribution functions (PDF),
3. moments of a PDF
4. and its pertinent variance
.
All these topics will be discussed at length below. We feel however that a brief explanation may
be appropriate in order to convey the strategy behind a Monte-Carlo calculation. Let us first
demistify the somewhat obscure concept of a random variable. The example we choose is the
classic one, the tossing of two dice, its outcome and the corresponding probability. In principle,
we could imagine being able to exactly determining the motion of the two dice, and with given
initial conditions determine the outcome of the tossing. Alas, we are not capable of pursuing
this ideal scheme. However, it does not mean that we do not have a certain knowledge of the
outcome. This partial knowledge is given by the probablity of obtaining a certain number when
tossing the dice. To be more precise, the tossing of the dice yields the following possible values
(9.1)
These values are called the domain. To this domain we have the corresponding probabilities
(9.2)
The numbers in the domain are the outcomes of the physical process tossing the dice. We cannot
tell beforehand whether the outcome is 3 or 5 or any other number in this domain. This defines
the randomness of the outcome, or unexpectedness or any other synonimous word which encom-
passes the uncertitude of the final outcome. The only thing we can tell beforehand is that say
the outcome 2 has a certain probability. If our favorite hobby is to spend an hour every evening
throwing dice and registering the sequence of outcomes, we will note that the numbers in the
above domain
(9.3)
appear in a random order. After 11 throws the results may look like
(9.4)