I am in trouble to implement the following double integral. there is a summation inside the integral which make things a bit complicated. The matlab code I did is as follows and always has error like "Matrix dimensions must agree." , any hint to implement it? thanks
n=3;
nn=1:n;
aa=gamma([1:n])
thre=3;
lapha=4;
r=3;
fun1= #(theta, x) (1-sum( lambda *pi *( (x-r).^2+r^2-(x-r).*r.*cos(theta)).^(nn-1)./aa).*exp(-1*lambda *pi*((x-r).^2+r^2-(x-r).*r.*cos(theta)))).*lambda/n*(1-1/2^n).*thre.*r.^alpha.*(x-r).^(1-alpha) ;
answer=integral2( fun1, 0, 2*pi, 0, inf )
The double integral:
Your problem is the way you calculate the sum in the integrated function. The documentation of intergal2 says that the function argument must accept arrays X and Y of the same size and return an array of corresponding values. But this expression inside the function definition:
((x-r).^2 + r^2 - (x-r).*r.*cos(theta)).^(nn-1)./aa
is not working the way you expect, because it's you who decide the size of nn and aa, while it's integral2 that decides the size of the vectors x and theta; no wonder that there are disagreements on it.
Related
I would love to know why this code is not working. if anyone has any information on what is causing matlab to find so many errors, it would be greatly appreciated.
m = 1;
c = 1.5;
fun =#(x, epsilon) 1 .* (1 - (1 - cos(x))/(2.*epsilon)).^c .* cos(m.*x);
a = #(ep) acos(1-(2*ep));
lm =#(e) 1/(2.*pi) .* integral(#(x)fun(x, e), -1.*a(e), a(e));
fprintf('ball bearing at 0.6 is %4.4f', lm(0.6));
the function that I am trying to replicate is πΌπ(π) =1/2πβ«[1 β (1 β cos(π₯))/2π]^π cos(ππ₯)dx
There should be no need for the dot modifier on the multiplication to my knowledge, however Matlab was complaining that this required element-wise operations even though there are no matrices involved.
According to the documentation of integral the function to be integrated must be vectorized:
For scalar-valued problems, the function y = fun(x) must accept a vector argument, x, and return a vector result, y. This generally means that fun must use array operators instead of matrix operators. For example, use .* (times) rather than * (mtimes).
I want to evaluate the simple example of integral
a = max(solve(x^3 - 2*x^2 + x ==0 , x));
fun = #(x) exp(-x.^2).*log(x).^2;
q = integral(fun,0,a)
and the error is
Error using integral (line 85)
A and B must be floating-point scalars.
Any tips? The lower limit of my integral must be a function, not a number.
The Matlab command solve returns symbolic result. integral accepts only numeric input. Use double to convert symbolic to numeric. As your code is written now, already max should throw an error due to symbolic input. The following works.
syms x;
a = max(double(solve(x^3 - 2*x^2 + x)));
fun = #(x) exp(-x.^2).*log(x).^2;
q = integral(fun,0,a)
Output: 1.9331.
the lower limit of my integral must be a function, not a number
integral is a numeric integration routine; the limits of integration must be numeric.
Check values of a by mouse over in breakpoint or removing the ; from the end of the line so it prints a. Based on the error, a is not a scalar float. You might need another max() or double() statement to transform the vector to a single value.
Solve Help : http://www.mathworks.com/help/symbolic/solve.html
Integral Help : http://www.mathworks.com/help/ref/integral.html
The following is a MATLAB problem.
Suppose I define an function f(x,y).
I want to calculate the partial derivative of f with respect to y, evaluated at a specific value of y, e.g., y=6. Finally, I want to integrate this new function (which is only a function of x) over a range of x.
As an example, this is what I have tried
syms x y;
f = #(x, y) x.*y.^2;
Df = subs(diff(f,y),y,2);
Int = integral(Df , 0 , 1),
but I get the following error.
Error using integral (line 82)
First input argument must be a function
handle.
Can anyone help me in writing this code?
To solve the problem, matlabFunction was required. The solution looks like this:
syms x y
f = #(x, y) x.*y.^2;
Df = matlabFunction(subs(diff(f,y),y,2));
Int = integral(Df , 0 , 1);
Keeping it all symbolic, using sym/int:
syms x y;
f = #(x, y) x.*y.^2;
Df = diff(f,y);
s = int(Df,x,0,1)
which returns y. You can substitute 2 in for y here or earlier as you did in your question. Not that this will give you an exact answer in this case with no floating-point error, as opposed to integral which calculated the integral numerically.
When Googling for functions in Matlab, make sure to pay attention what toolbox they are in and what classes (datatypes) they support for their arguments. In some cases there are overloaded versions with the same name, but in others, you may need to look around for a different method (or devise your own).
I am writing a program in Matlab and I have a function defined this way.
sum (i=1...100) (a*x(i) + b*y(i) + c)
x and y are known, while a, b and c are not: I need to find values for them such that the total value of the function is minimized. There is no additional constraint for the problem.
I thought of using fminsearch to solve this minimization problem, but from Mathworks I get that functions which are suitable inputs for fminsearch are defined like this (an example):
square = #(x) x.^2
So in my case I could use a vector p=[a, b, c] as the value to minimize, but then I don't know how to define the remaining part of the function. As you can see the number of possible values for the index i is huge, so I cannot simply sum everything together explicitly, but I need to represent the summation in some way. If I write the function somewhere else then I am forced to use symbolic calculus for a, b and c (declaring them with syms) and I'm not sure fminsearch would accept that.
What can I do? Of course if fminsearch turns out to be unfeasible for my situation I accept links to use something else.
The most general solution is to use x and y in the definition of the objective function:
>> objfun = #(p) sum( p(1).*x + p(2).*y + p(3) );
>> optp = fminsearch( objfun, po, ... );
I want to evaluate a double integral of the form
$$\int_{-\infty}^a \int_{-\infty}^b \sum_{i,j}^K a_ia_jx^iy^j\exp(-x^2 - y^2 + xy)dx dy $$
where $a_i$ and $a_j$ are constants. Since the integral is linear, I can interchange summation and integration, but in this case I have to evaluate $K^2$ integrals and it takes too long. In that case I do the following:
for i = 1:K
for j = 1:K
fun = #(x,y) x.^i.*y.^j.*exp(-2.*(x.^2 + y.^2 - 2.*x.*y))
part(i,j) = alpha(i)*alpha(j)*integral2(fun,-inf,a,-inf,b)
end
end
It takes too long, so I want to evaluate only one integral, but I don't know how to vectorize $\sum_{i,j}^K a_ia_jx^iy^j\exp(-x^2 - y^2 + xy)$, namely, how to supply it to integral2. I would be very grateful for any help.
It looks like you'll need to have i and j be third and fourth dimensions, in order for there to be a chance that the code will work.
I also don't have integral2 (I use octave, integral2 is a new matlab function that octave doesn't yet have), so I can't test it, but I'd think something like this might work:
alphaset=zeros(1,1,K,K);
alphaset(1,1,1:K,1:K)=alpha(1:K)'*alpha(1:K);
i_set=zeros(1,1,K,1);
j_set=zeros(1,1,1,K);
i_set(:)=1:K;
j_set(:)=1:K;
fun=#(x,y) x.^i_set.*y.^j_set.*exp(-2.*(x.^2 + y.^2 - 2.*x.*y));
part = squeeze(alphaset.*integral2(fun,-inf,a,-inf,b));
As I said, I can't promise that it'll work, because I don't know how integral2 works. But if you replace the integral2 with simply "sum(sum(fun([1,2,4],[3,-1,2])))", then it works as intended for that operation (that is, it sums over the x and y values, and the result is a matrix over the set of indices).
If you just want to improve speed, you may try parfor.
Let $X=(x,x^2,\cdots,x^K)$, $Y=(y,y^2,\cdots,y^K)$, $A=(a_{ij})$ be a matrix with $a_{ij}=a_{i}a_{j}$, then
$$\sum_{i,j}^K a_{i}a_{j}x^iy^j=XAY^{T}$$
I don't have integral2 function on my matlab, so I didn't test if it will improve the speed a lot.
Also, I think you need to use syms x and y, after you compute the $$XAY^{T}$$, then use matlabFunction to convert symbolic expression to function handle. Here it is my test code: syms x y; X=[x,x^2]; Y=[y,y^2]; Z=X*Y'; fun =matlabFunction(Z); ff=#(x,y) x^2+y^2; gg=fun(x,y).*ff(x,y);