Is There a Numerical Method to Calculate the Minimum of a Functional?

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Numerical methods exist for calculating the minimum of a functional, including the finite difference method, finite element method, and others. The finite difference method approximates functional derivatives by evaluating nearby functions and can be considered recursive. The finite element method discretizes the functional into elements for analysis, commonly used in engineering applications. Other methods, such as spectral and boundary element methods, also provide alternatives for calculating functional derivatives. Choosing the right method depends on the specific problem being addressed.
eljose
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Let be F the functional given by

J(y)=J(x,y,dy/dx)

my question is,apart from the usual definition of functional derivative and Lagrange equation,..does a numerical method exist to calculate it,i mean am looking for a recursive method,you introduce an initial function y0(x) from this you get a function y1(x) and os on with the limit with n tending to infinty taht d(yn(x),yn+1(x))=0 as n tends to infintiy
 
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Yes it can be done, in the days before the tidy euler-lagrange equation we are presented with, Euler represented a functional as a regular function of infinitely many variables, and differentiated wrt to each variable to find a stationary point.
 


Yes, there are numerical methods available for calculating functional derivatives. One such method is the finite difference method, where the functional derivative is approximated by the finite difference between two nearby functions. This method is often used in computational fluid dynamics and other fields of engineering.

Another method is the finite element method, where the functional is discretized into a finite number of elements and the functional derivative is calculated for each element. This method is commonly used in structural analysis and other fields of engineering.

There are also other methods such as the spectral method, the boundary element method, and the variational method, which can be used to calculate functional derivatives. Each method has its own advantages and limitations, and the choice of method depends on the specific problem being solved.

In terms of a recursive method, the finite difference method can be considered as a form of recursion, where the functional derivative is calculated iteratively until the desired level of accuracy is achieved. However, other methods such as the variational method and the finite element method may not be easily formulated as recursive methods.

In conclusion, there are various numerical methods available for calculating functional derivatives, each with its own advantages and limitations. It is important to choose the appropriate method for the specific problem at hand.
 
There are probably loads of proofs of this online, but I do not want to cheat. Here is my attempt: Convexity says that $$f(\lambda a + (1-\lambda)b) \leq \lambda f(a) + (1-\lambda) f(b)$$ $$f(b + \lambda(a-b)) \leq f(b) + \lambda (f(a) - f(b))$$ We know from the intermediate value theorem that there exists a ##c \in (b,a)## such that $$\frac{f(a) - f(b)}{a-b} = f'(c).$$ Hence $$f(b + \lambda(a-b)) \leq f(b) + \lambda (a - b) f'(c))$$ $$\frac{f(b + \lambda(a-b)) - f(b)}{\lambda(a-b)}...

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