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e.g. ∫f{y,y' ;x}dx

why we treat y and y' independent ?

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- Thread starter HAMJOOP
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In summary, the conversation discusses the use of Euler's equation in calculus of variation to minimize integrals. It is mentioned that there is no algebraic relation between a function and its derivative, which is why boundary conditions are necessary to solve differential equations. However, it is argued that the real reason for treating y and y' as independent is to obtain an expression for the difference in terms of partial derivatives. Additionally, it is noted that while y and y' may appear to be independent variables, they are actually dependent on the function f(x) being sought.

- #1

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e.g. ∫f{y,y' ;x}dx

why we treat y and y' independent ?

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- #2

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This is why you need boundary conditions to solve differential equations.

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UltrafastPED said:

This is why you need boundary conditions to solve differential equations.

Sorry, but this is a bogus answer. A function may depend on another function non-algebraically, and that is perfectly fine as far as functional dependency goes. Not to mention that the dependency may perfectly well be algebraic.

The real reason is that we use the partial derivatives to obtain an expression for the difference ## F(z + \Delta z, y + \Delta y, x) - F(z, y, x) ##, which is approximately ## F_z \Delta z + F_y \Delta y ## when ##\Delta z## and ##\Delta y## are sufficiently small. This expression is true generally, and is true when ## z ## represents the derivative of ## y ## - all it takes is that the variations of both must be small enough. If ## y = f(x) ##, its variation is ## \delta y = \epsilon g(x) ##, and ## \delta y' = \epsilon g'(x)##. If ## \epsilon ## is small enough, then using the result above, ## F((y + \delta y)', (y + \delta y), x) - F(y', y, x)) \approx \epsilon F_{y'}g'(x) + \epsilon F_y g(x) ##, where ##F_{y'}## is just a fancy symbol equivalent to ##F_z##, meaning partial differentiation with respect to the first argument. Then we use integration by parts and convert that to ## \epsilon (-(F_{y'})' + F_y) g(x)##. Observe that we

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We want to find the least action for:

##S = \int_{x_1}^{x_2} f(y,y',x) \, dx##

While this may look as though y, y' and x are simple independent variables, since we are actually looking for the function f(x) that provides this least action, what this notation really means is this:

##S = \int_{x_1}^{x_2} f[y(x), \frac d {dx} y(x), x] \, dx##

So y and y' are not truly independent.

The calculus of variations is a branch of mathematics that deals with finding the optimal value of a function, also known as the extremum, by considering variations in the function. It is used to solve problems involving optimization, such as finding the shortest path or minimizing energy usage.

The main difference between the two is that traditional calculus deals with finding the maxima and minima of a function with a fixed set of variables, while the calculus of variations considers variations in the function itself. Traditional calculus is also concerned with finding exact solutions, whereas the calculus of variations focuses on finding the optimal solution within a certain range of values.

The calculus of variations has various applications in physics, engineering, economics, and other fields. Some examples include finding the path of least resistance in fluid dynamics, determining the shape of a hanging chain, and optimizing the shape of an airplane wing for maximum lift.

The Euler-Lagrange equation is a fundamental equation in the calculus of variations that is used to find the extremum of a functional. It relates the derivative of the function to the function itself and its derivatives. Solving this equation yields the optimal solution for the given problem.

While the calculus of variations is a powerful tool for solving optimization problems, it does have some limitations. It can only be applied to functions that have a smooth and continuous behavior, and it does not always guarantee finding the global extremum. Additionally, some problems may have no solution or an infinite number of solutions, making it difficult to determine the optimal value.

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