How do I minimize a function with a constraint using Lagrange-Euler method?

fery
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I am working on a functional and I need to find its minimum, the conventional procedure is to use Lagrange-Euler method and find the minimum state of the function, but if I need to impose a constraint to the function, I don't know what I need to do

J=int(F(t, f(t), a, b)) minimize(f) and int(G(t, f(t), a, b))=M,
It should be very elementary, but I am confused about what I need to do.

Your help will be very appreciated.
Farshad
 
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I don't understand your notation. See if I've guess the problem correctly:

J(f) is a linear functional defined by J(f) = \int_a^b F(x,f(x)) dx where F(x,y) is a given function of two variables. We wish to find the minimum value of J over all functions f that satisfy \int_a^b G(t,f(t)) = M where M is a given constant and G(x,y) is a given function of two variables.
 
All true but F(x,f(x)) is a functional not a function, which is mapping of a function to R. For minimization of the functional Euler-Lagrange is the conventional method, but when there is constraint (int(G(t,f(t),a,b)=M) I am not sure what should I do.

Farshad
 
fery said:
All true but F(x,f(x)) is a functional not a function,

Then I don't understand the notation F(x,f(x)). If F is a functional and f is a function then
F(f) is a real number correct? We don't need the argument 'x'.

For example, in the calculus of variations an arc length problem is to minimize the functional J given by
J[f] = \int_a^b \sqrt{1 + (f'(x))^2} dx
The expression \sqrt{1 + (f'(x))^2)} is a function not a functional.
 
I agree, the integrand does not return a number given a function. It returns an expression. You integrate and then you have a number.

You might look up the 'isoperimetric problem' which is an example, or 'variational problems with subsidiary conditions' more generally. Gelfand and Fomin's little book on the calculus of variations has a section on it.

that is, Given the functional:

<br /> J[y]=\int_a^b F(x,y,y&#039;)dx<br />
let the admissable curves satisfy the conditions:

<br /> y(a)=A,y(b)=B, K[y]=\int_a^b G(x,y,y&#039;)dx=M<br />

Where K[y] is another functional and let J[y] have an extremum for y=y(x). Then, if y=y(x) is not an extremal of K[y], there exists a constant \lambda such that y=y(x) is an extremal of the functional:

<br /> \int_a^b (F+\lambda G)dx<br />

That's from the text and probably is enough to get you started.
 
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