What is the gradient vector problem for a function with dependent variables?

In summary, the problem involves finding the gradient of a function f given that x = r + t and y = e^{rt}. The solution involves taking the partial derivatives of f with respect to x and y, and substituting r and t in their respective places. There may be ambiguity in the notation used, but the calculated answer is most likely correct.
  • #1
evo_vil
10
0

Homework Statement



If [itex]z = f(x,y)[/itex] such that [itex]x = r + t[/itex] and [itex]y = e^{rt}[/itex], then determine [itex]\nabla f(r,t)[/itex]

Homework Equations



[itex]\nabla f(x,y) = <f_x,f_y>[/itex]

The Attempt at a Solution



Now if i follow this the way i think it should be done then i find the partials of f wrt x and y and then simply sub in r and t in the place of x and y respectively...

But if i get del f the normal way i get:

[itex]\nabla f = <f_x+f_y t e^{rt},f_x+f_y r e^{rt}>[/itex]

is this the final/correct answer or am i missing a trick question where i was asked to find del f(r,t) and not del f(x,y)
 
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  • #2
Welcome to FP, evo_vil! :smile:

Your problem is ambiguous.
If I take it very literal, the answer would be:
[tex]\nabla f(r,t)=<f_x(r,t), f_y(r,t)>[/tex]

However, I can't imagine that this was intended.

I expect that you're supposed to take the gradient from a function f* defined by:
f*(r,t) = f(x(r,t), y(r,t)).
It is not unusual that this function f* is simply called f, although that is ambiguous.

This is what you calculated, and no doubt correct.
 
  • #3
Thanks!

Ive browsed PF for quite a few years, but never participated, so thanks for the welcome...

I think I am just going to go with what I've calculated and see how it goes...
Maybe see if other people get the same thing.

Thanks for your help
 

Related to What is the gradient vector problem for a function with dependent variables?

What is a gradient vector problem?

A gradient vector problem is a mathematical problem that involves finding the direction and magnitude of the maximum change in a function. It is commonly used in optimization and machine learning.

What is the purpose of solving a gradient vector problem?

The purpose of solving a gradient vector problem is to find the steepest ascent or descent direction for a given function. This is useful in finding the maximum or minimum points of a function, which can be used in optimization or prediction tasks.

What are some common methods for solving a gradient vector problem?

Some common methods for solving a gradient vector problem include gradient descent, conjugate gradient method, and Newton's method. These methods use iterative processes to find the optimal solution.

Can a gradient vector problem have multiple solutions?

Yes, a gradient vector problem can have multiple solutions. This can occur when the function has multiple local maxima or minima. In this case, the solution found will depend on the starting point of the optimization process.

What are some real-world applications of gradient vector problems?

Gradient vector problems have many real-world applications, including in machine learning, image and signal processing, economics, and engineering. They are used to optimize processes and predict outcomes by finding the maximum or minimum points of a function.

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