# Euler's Method of Numerical Approximations

• MATLAB
• kishtik
In summary: I try to be careful, but I guess I'm not careful enough. Thanks!In summary, the conversation discusses the algorithm for Euler's Method of Numerical Approximations and how to implement it in MATLAB. The algorithm involves defining a function, inputting initial values, and using a for loop to calculate the values of t and y. The code provided in the conversation is modified to use an inline function to evaluate the next k values in each iteration of the loop. However, a small mistake in the code results in a significant error in the output. After fixing the mistake, the code produces the expected results.
kishtik
Hello,
For Euler's Method of Numerical Approximations, my book (Boyce&DiPrima)
gives this algorithm:

Step 1: define f(t,y)
Step 2: input initial values t0 and y0
Step 3: input step size h and number of steps n
Step 4: output t0 and y0
Step 5: for j from 1 to n do
Step 6: k1 = f(t,y)
y = y + h*k1
t = t + h
Step 7: output t and y
Step 8: end

I tried this on Matlab. My code was:

t=input('Enter t0:');
y=input('Enter y0:');
h=input('Enter step size h:');
n=input('Enter number of steps n:');
k1=input('Define f(t,y):');
result=zeros(n,2);
result(1)=t;
result(1,2)=y;
for j=[2:n+1]
k=k1;
y=y+k*h;
t=t+h;
result(j,1)=t;
result(j,2)=y;
end
disp(' t y');
disp(result);

But I realize that my program calculates k1 only for the initial values... How can I change it so that k1 is actually calculated in every turn of for loop?

Thanks.

Last edited:
You need to evaluate k = f(t,y) INSIDE the for loop. This way each time you change t and y, you will get a new value for k.

kishtik said:
t=input('Enter t0:');
y=input('Enter y0:');
h=input('Enter step size h:');
n=input('Enter number of steps n:');
k1=input('Define f(t,y):');
result=zeros(n,2);
result(1)=t;
result(1,2)=y;
for j=[2:n+1]
k=k1;

k = f(t,y)
y=y+k*h;
t=t+h;
result(j,1)=t;
result(j,2)=y;
end
disp(' t y');
disp(result);

Of course, but the value of k1 is evaluated and assigned at the line
k1=input('Define f(t,y):');
before the loop and the information about the function which should be used to evaluate k's is lost... How can I change it so that it actually evaluates k in the loop using the input k1 as a formula rather than the first of k's?

(Edit: 100th post btw.)

kishtik said:
Of course, but the value of k1 is evaluated and assigned at the line
k1=input('Define f(t,y):');
before the loop and the information about the function which should be used to evaluate k's is lost... How can I change it so that it actually evaluates k in the loop using the input k1 as a formula rather than the first of k's?

(Edit: 100th post btw.)

Ok, I thought your input k1 was just the initial value, but you're saying
k1=input('Define f(t,y):');
is supposed to take in a symbolic function f(t,y)?

Yes, and that symbolic expression should be used to compute the next k's in every turn of the loop.

kishtik said:
Yes, and that symbolic expression should be used to compute the next k's in every turn of the loop.

Ok, I think you want to use inline functions. If you use
k1=input('Define f(t,y):')
and enter something like 't^3+4-5*y' with the ' ', then after that put
f = inline(k1);
and it should define f(t,y) = t^3+4-5*y. Then replace k=k1 with k=f(t,y) insinde your loop. I think that will do what you want.

Code:
t=input('Enter t0:');
y=input('Enter y0:');
h=input('Enter step size h:');
n=input('Enter number of steps n:');
k1=input('Define f(t,y):');
f = inline(k1);
result=zeros(n,2);
result(1)=t;
result(1,2)=y;
for j=[2:n+1]
k=f(t,y);
y=y+k*h;
t=t+h;
result(j,1)=t;
result(j,2)=y;
end
disp(' t y');
disp(result);

Thank you! That was just what I was looking for. But now I have another problem...
I modified the code to:

t=input('Enter t0:');
y=input('Enter y0:');
h=input('Enter step size h:');
n=input('Enter number of steps n:');
k1=input('Define f(t,y) in single quotation marks:');
f=inline(k1,'y','t');
result=zeros(n,2);
result(1)=t;
result(1,2)=y;
for j=[2:n+1]
k=f(t,y);
y=y+k*h;
t=t+h;
result(j,1)=t;
result(j,2)=y;
end
disp(' t y');
disp(result);

And tried the example in the book(p.103) with,
t0 = 0,
y0 =1,
h=0.1,
n=4,
f(t,y)=3 + exp(-t) - (1/2)*y,
and got this out:
t _________________y
0________________1.00000000000000
0.10000000000000 1.33678794411714
0.20000000000000 1.65805675248899
0.30000000000000 1.96710763513263
0.40000000000000 2.26609371515479

but the book gave

y0 = 1
y1 = 1.3500
y2 = 1.6730
y3 = 1.9712
y4 = 2.2467

Nearly %2 error for every y. Isn't this too much for an error from rounding or "how my program is constructed" or "how my computer executes arithmetic steps"? The book says "minor variations in the last decimal place" are acceptable, but %2?

kishtik said:
k1=input('Define f(t,y) in single quotation marks:');
f=inline(k1,'y','t');
.
.
.
k=f(t,y);

It looks like you have defined f(y,t) and are evaluating f(t,y). So switch one of the two and it should work.

It does.

## 1. What is Euler's Method of Numerical Approximations?

Euler's Method is a mathematical algorithm used to approximate the solutions to differential equations. It was developed by Swiss mathematician Leonhard Euler in the 18th century.

## 2. How does Euler's Method work?

Euler's Method works by breaking down a differential equation into smaller, simpler steps. It calculates the slope of the curve at a given point and uses that information to estimate the value at the next point.

## 3. What are the advantages of using Euler's Method?

Euler's Method is relatively simple and easy to implement, making it a popular choice for solving differential equations. It also provides a good approximation of the solution, especially when the step size is small.

## 4. What are the limitations of Euler's Method?

One of the main limitations of Euler's Method is that it can only provide an approximation of the solution, and the accuracy of the approximation depends on the step size used. If the step size is too large, the approximation can be significantly off.

## 5. How is Euler's Method used in real-world applications?

Euler's Method is commonly used in physics, engineering, and other fields to solve differential equations that model real-world phenomena. It can also be used to simulate systems and predict future behaviors based on current conditions.

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