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

In summary, the problem is to minimize the functional J[f] = ∫(F(x,f(x))) dx over all functions f that satisfy ∫(G(x,f(x))) dx = M. This can be solved using the Euler-Lagrange method, but if there is an additional constraint ∫(G(x,f(x))) dx = M, then a constant λ can be introduced to find an extremal of the functional ∫(F+λG) dx.
  • #1
fery
10
0
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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  • #2
I don't understand your notation. See if I've guess the problem correctly:

[tex] J(f) [/tex] is a linear functional defined by [tex] J(f) = \int_a^b F(x,f(x)) dx [/tex] where [tex] F(x,y) [/tex] is a given function of two variables. We wish to find the minimum value of [tex] J [/tex] over all functions [tex] f [/tex] that satisfy [tex] \int_a^b G(t,f(t)) = M [/tex] where [tex] M [/tex] is a given constant and [tex] G(x,y) [/tex] is a given function of two variables.
 
  • #3
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
 
  • #4
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 [tex] J [/tex] given by
[tex] J[f] = \int_a^b \sqrt{1 + (f'(x))^2} dx [/tex]
The expression [tex] \sqrt{1 + (f'(x))^2)} [/tex] is a function not a functional.
 
  • #5
  • #7
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:

[tex]
J[y]=\int_a^b F(x,y,y')dx
[/tex]
let the admissable curves satisfy the conditions:

[tex]
y(a)=A,y(b)=B, K[y]=\int_a^b G(x,y,y')dx=M
[/tex]

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 [tex]\lambda[/tex] such that y=y(x) is an extremal of the functional:

[tex]
\int_a^b (F+\lambda G)dx
[/tex]

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

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

1. What is functional minimization?

Functional minimization is a mathematical technique used to find the minimum value of a mathematical function. It involves finding the input values that result in the smallest output value for a given function.

2. How is functional minimization used in science?

Functional minimization is used in many areas of science, including physics, chemistry, and engineering. It is often used to optimize processes and systems by finding the most efficient solution.

3. What are some common methods for functional minimization?

Some common methods for functional minimization include gradient descent, Newton's method, and genetic algorithms. These methods use different approaches to find the minimum value of a function.

4. What are the benefits of using functional minimization?

The benefits of using functional minimization include increased efficiency, improved accuracy, and the ability to solve complex problems that may not have analytical solutions. It also allows for the optimization of systems and processes in various scientific fields.

5. Are there any limitations to functional minimization?

While functional minimization is a powerful tool, it does have some limitations. It may not always find the global minimum of a function, and the results can be sensitive to the initial conditions and parameters chosen. It also requires a good understanding of the function being minimized and careful consideration of the chosen method.

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