Numerical Approximation of Multiple Integrals

In document Vector Calculus (Page 121-125)

3.4 Numerical Approximation of Multiple Integrals

As you have seen, calculating multiple integrals is tricky even for simple functions and regions. For complicated functions, it may not be possible to evaluate one of the iterated in- tegrals in a simple closed form. Luckily there are numerical methods for approximating the value of a multiple integral. The method we will discuss is called theMonte Carlo method. The idea behind it is based on the concept of the average value of a function, which you learned in single-variable calculus. Recall that for a continuous function f(x), theaverage value f¯of f over an interval [a,b] is defined as

¯ f = 1 ba Zb a f(x)dx. (3.11)

The quantity ba is the length of the interval [a,b], which can be thought of as the “volume” of the interval. Applying the same reasoning to functions of two or three variables, we define theaverage valueoff(x,y) over a regionRto be

¯ f = 1 A(R) Ï R f(x,y)d A, (3.12)

whereA(R) is the area of the regionR, and we define theaverage valueof f(x,y,z) over a solidSto be ¯ f = 1 V(S) Ñ S f(x,y,z)dV , (3.13)

whereV(S) is the volume of the solidS. Thus, for example, we have

Ï

R

f(x,y)d A = A(R) ¯f . (3.14)

The average value of f(x,y) over R can be thought of as representing the sum of all the values off divided by the number of points inR. Unfortunately there are an infinite number (in fact, uncountablymany) points in any region, i.e. they can not be listed in a discrete sequence. But what if we took a very large number N of random points in the region R (which can be generated by a computer) and then took the average of the values of f for those points, and used that average as the value of ¯f? This is exactly what the Monte Carlo method does. So in formula (3.14) the approximation we get is

Ï R f(x,y)d A A(R) ¯f±A(R) s f2( ¯f)2 N , (3.15) where ¯ f = PN i=1f(xi,yi) N and f 2 = PN i=1(f(xi,yi))2 N , (3.16)

with the sums taken over the N random points (x1,y1), . . ., (xN,yN). The ±“error term” in formula (3.15) does not really provide hard bounds on the approximation. It represents a singlestandard deviationfrom theexpectedvalue of the integral. That is, it provides alikely bound on the error. Due to its use of random points, the Monte Carlo method is an example of a probabilisticmethod (as opposed to deterministicmethods such as Newton’s method, which use a specific formula for generating points).

For example, we can use formula (3.15) to approximate the volume V under the plane z=8x+6yover the rectangle R=[0, 1]×[0, 2]. In Example 3.1 in Section 3.1, we showed that the actual volume is 20. Below is a code listing (montecarlo.java) for a Java program that calculates the volume, using a number of pointsN that is passed on the command line as a parameter.

//Program to approximate the double integral of f(x,y)=8x+6y //over the rectangle [0,1]x[0,2].

public class montecarlo {

public static void main(String[] args) {

//Get the number N of random points as a command-line parameter

int N = Integer.parseInt(args);

double x = 0; //x-coordinate of a random point

double y = 0; //y-coordinate of a random point

double f = 0.0; //Value of f at a random point

double mf = 0.0; //Mean of the values of f

double mf2 = 0.0; //Mean of the values of f^2

for (int i=0;i<N;i++) { //Get the random coordinates x = Math.random(); //x is between 0 and 1

y = 2 * Math.random(); //y is between 0 and 2 f = 8*x + 6*y; //Value of the function mf = mf + f; //Add to the sum of the f values mf2 = mf2 + f*f; //Add to the sum of the f^2 values }

mf = mf/N; //Compute the mean of the f values mf2 = mf2/N; //Compute the mean of the f^2 values

System.out.println("N = " + N + ": integral = " + vol()*mf + " +/- " + vol()*Math.sqrt((mf2 - Math.pow(mf,2))/N)); //Print the result }

//The volume of the rectangle [0,1]x[0,2]

public static double vol() {

return 1*2;

} }

Listing 3.1 Program listing for montecarlo.java

The results of running this program with various numbers of random points (e.g. java montecarlo 100) are shown below:

3.4 Numerical Approximation of Multiple Integrals 115 N = 10: 19.36543087722646 +/- 2.7346060413546147 N = 100: 21.334419561385353 +/- 0.7547037194998519 N = 1000: 19.807662237526227 +/- 0.26701709691370235 N = 10000: 20.080975812043256 +/- 0.08378816229769506 N = 100000: 20.009403854556716 +/- 0.026346782289498317 N = 1000000: 20.000866994982314 +/- 0.008321168748642816

As you can see, the approximation is fairly good. As N→ ∞, it can be shown that the Monte Carlo approximation converges to the actual volume (on the order ofO(pN), in com- putational complexity terminology).

In the above example the region R was a rectangle. To use the Monte Carlo method for a nonrectangular (bounded) regionR, only a slight modification is needed. Pick a rectangle

˜

R that enclosesR, and generate random points in that rectangle as before. Then use those points in the calculation of ¯f only if they are insideR. There is no need to calculate the area ofR for formula (3.15) in this case, since the exclusion of points not insideR allows you to use the area of the rectangle ˜Rinstead, similar to before.

For instance, in Example 3.4 we showed that the volume under the surface z=8x+6y over the nonrectangular regionR={(x,y) : 0x1, 0y2x2}is 6.4. Since the rectangle

˜

R=[0, 1]×[0, 2] containsR, we can use the same program as before, with the only change being a check to see if y<2x2 for a random point (x,y) in [0, 1]×[0, 2]. Listing 3.2 below contains the code (montecarlo2.java):

//Program to approximate the double integral of f(x,y)=8x+6y over the //region bounded by x=0, x=1, y=0, and y=2x^2

public class montecarlo2 {

public static void main(String[] args) {

//Get the number N of random points as a command-line parameter

int N = Integer.parseInt(args);

double x = 0; //x-coordinate of a random point

double y = 0; //y-coordinate of a random point

double f = 0.0; //Value of f at a random point

double mf = 0.0; //Mean of the values of f

double mf2 = 0.0; //Mean of the values of f^2

for (int i=0;i<N;i++) { //Get the random coordinates x = Math.random(); //x is between 0 and 1

y = 2 * Math.random(); //y is between 0 and 2

if (y < 2*Math.pow(x,2)) { //The point is in the region f = 8*x + 6*y; //Value of the function

mf = mf + f; //Add to the sum of the f values mf2 = mf2 + f*f; //Add to the sum of the f^2 values }

}

mf = mf/N; //Compute the mean of the f values mf2 = mf2/N; //Compute the mean of the f^2 values

" +/- " + vol()*Math.sqrt((mf2 - Math.pow(mf,2))/N)); }

//The volume of the rectangle [0,1]x[0,2]

public static double vol() {

return 1*2;

} }

Listing 3.2 Program listing for montecarlo2.java

The results of running the program with various numbers of random points (e.g. java montecarlo2 1000) are shown below:

N = 10: integral = 6.95747529014894 +/- 2.9185131565120592 N = 100: integral = 6.3149056229650355 +/- 0.9549009662159909 N = 1000: integral = 6.477032813858756 +/- 0.31916837260973624 N = 10000: integral = 6.349975080015089 +/- 0.10040086346895105 N = 100000: integral = 6.440184132811864 +/- 0.03200476870881392 N = 1000000: integral = 6.417050897922222 +/- 0.01009454409789472

To use the Monte Carlo method to evaluate triple integrals, you will need to generate random triples (x,y,z) in a parallelepiped, instead of random pairs (x,y) in a rectangle, and use the volume of the parallelepiped instead of the area of a rectangle in formula (3.15) (see Exercise 2). For a more detailed discussion of numerical integration methods, see PRESSet al.

C

1. Write a program that uses the Monte Carlo method to approximate the double integral

Î

R

ex yd A, whereR=[0, 1]×[0, 1]. Show the program output forN=10, 100, 1000, 10000, 100000 and 1000000 random points.

2. Write a program that uses the Monte Carlo method to approximate the triple integral

Ð

S

ex yzdV, where S=[0, 1]×[0, 1]×[0, 1]. Show the program output for N =10, 100, 1000, 10000, 100000 and 1000000 random points.

3. Repeat Exercise 1 with the regionR={(x,y) :1x1, 0yx2}.

4. Repeat Exercise 2 with the solidS={(x,y,z) : 0x1, 0y1, 0z1xy}.

5. Use the Monte Carlo method to approximate the volume of a sphere of radius 1.

In document Vector Calculus (Page 121-125)
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