The
for loop allows us to repeat certain commands. If you want to repeat some action in a predetermined way, you can use the
for loop. All of the loop structures in matlab are started with a keyword such as "for", or "while" and they all end with the word "end". Another deep thought, eh.
The
for loop is written around some set of statements, and you must tell Matlab where to start and where to end. Basically, you give a vector in the "for" statement, and Matlab will loop through for each value in the vector:
For example, a simple loop will go around four times each time changing a loop variable,
j:
كود:
>> for j=1:4,
j
end
j =
1
j =
2
j =
3
j =
4
>>
When Matlab reads the "for" statement it constructs a vector, [1:4], and
j will take on each value within the vector in order. Once Matlab reads the "end" statement, it will execute and repeat the loop. Each time the for statement will update the value of
j and repeat the statements within the loop. In this example it will print out the value of j each time.
For another example, we define a vector and later change the entries. Here we step though and change each individual entry:
كود:
>> v = [1:3:10]
v =
1 4 7 10
>> for j=1:4,
v(j) = j;
end
>> v
v =
1 2 3 4
Note, that this is a simple example and is a nice demonstration to show you how a
for loop works. However,
DO NOT DO THIS IN PRACTICE!!!! Matlab is an interpreted ******** and looping through a vector like this is the slowest possible way to change a vector. The notation used in the first statement is much faster than the loop.
A better example, is one in which we want to perform operations on the rows of a matrix. If you want to start at the second row of a matrix and subtract the previous row of the matrix and then repeat this operation on the following rows, a
for loop can do this in short order:
كود:
>> A = [ [1 2 3]' [3 2 1]' [2 1 3]']
A =
1 3 2
2 2 1
3 1 3
>> B = A;
>> for j=2:3,
A(j,:) = A(j,:) - A(j-1,:)
end
A =
1 3 2
1 -1 -1
3 1 3
A =
1 3 2
1 -1 -1
2 2 4
For a more realistic example, since we can now use loops and perform row operations on a matrix, Gaussian Elimination can be performed using only two loops and one statement:
>>
كود:
for j=2:3,
for i=j:3,
B(i,:) = B(i,:) - B(j-1,:)*B(i,j-1)/B(j-1,j-1)
end
end
B =
1 3 2
0 -4 -3
3 1 3
B =
1 3 2
0 -4 -3
0 -8 -3
B =
1 3 2
0 -4 -3
0 0 3
Another example where loops come in handy is the approximation of differential equations. The following example approximates the D.E. y'=x^2-y^2, y(0)=1, using Euler's Method. First, the step size, h, is defined. Once done, the grid points are found, and an approximation is found. The approximation is simply a vector, y, in which the entry y(j) is the approximation at x(j).
كود:
>> h = 0.1;
>> x = [0:h:2];
>> y = 0*x;
>> y(1) = 1;
>> size(x)
ans =
1 21
>> for i=2:21,
y(i) = y(i-1) + h*(x(i-1)^2 - y(i-1)^2);
end
>> plot(x,y)
>> plot(x,y,'go')
>> plot(x,y,'go',x,y)
While Loops
If you don't like the
for loop, you can also use a
while loop. The
while loop repeats a sequence of commands as long as some condition is met. This can make for a more efficient algorithm. In the previous example the number of time steps to make may be much larger than 20. In such a case the
for loop can use up a lot of memory just creating the vector used for the index. A better way of implementing the algorithm is to repeat the same operations but only as long as the number of steps taken is below some threshold. In this example the D.E. y'=x-|y|, y(0)=1, is approximated using Euler's Method:
>>
كود:
h = 0.001;
>> x = [0:h:2];
>> y = 0*x;
>> y(1) = 1;
>> i = 1;
>> size(x) ans = 1 2001
>> max(size(x)) ans = 2001
>> while(i<max(size(x))) y(i+1) = y(i) + h*(x(i)-abs(y(i))); i = i + 1; end >> plot(x,y,'go')
>> plot(x,y)
