Calculating the Gradient of f(x,y): A Step-by-Step Walkthrough

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In summary: The normal to the surface is the vector with components (-8,-1,1), which intersects the tangent plane at a right angle where it is shown in red. The plane appears to be tangent to the surface and parallel to the x,y plane, which is consistent with the fact that the partial derivatives of the function f(x,y) = y^2 + xy - x^2 + 2 with respect to x and y at the point (3,-2) are both zero, as is the gradient of the level curve.In summary, the conversation was about finding the gradient of the normal to a level curve at a given point. The correct answer was 1/8, but the term "gradient" caused some confusion as
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ac2707
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Ok, this is probably easy...but I'm stuck
f(x,y) = y^2 + xy - x^2 +2
find the gradient of the normal to the level curve at the point (3,-2)
my answer is -1/root65 , but it's supposed to be 1/8.

I did it by finding Fx(x,y) = y-2x and Fy(x,y) = 2y+x then finding the absolute value of the gradient..
 
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You got the gradient vector correct, you must have made a mistake in translating it to the plane equation. This is a great page for help on this:

http://tutorial.math.lamar.edu/AllBrowsers/2415/GradientVectorTangentPlane.asp
 
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It was probably the use of the word "gradient" that was confusing you- it confused me! One use of the word "gradient" is: gradient f(x,y) is the vector with components fx and fy. Of course, the gradient vector is perpendicular to a level curve. You correctly found that, for this function, that is (y-2x, 2y+x) and, at x= 3, y= -2, (-8,-1). The length of that (I wouldn't say "absolute value" for a vector) is [tex]sqrt{65}[/tex].

However, "gradient" is also often used for "slope": rise over run, of a line or vector. Here the "rise", the y component of the vector, is -1 and the "run", the x component of the vector, is -8. The "gradient" in this sense is -1/(-8)= 1/8.

Personally, I think its a bad choice of words.
 
  • #4
HallsofIvy said:
It was probably the use of the word "gradient" that was confusing you- it confused me! One use of the word "gradient" is: gradient f(x,y) is the vector with components fx and fy. Of course, the gradient vector is perpendicular to a level curve. You correctly found that, for this function, that is (y-2x, 2y+x) and, at x= 3, y= -2, (-8,-1). The length of that (I wouldn't say "absolute value" for a vector) is [tex]sqrt{65}[/tex].

However, "gradient" is also often used for "slope": rise over run, of a line or vector. Here the "rise", the y component of the vector, is -1 and the "run", the x component of the vector, is -8. The "gradient" in this sense is -1/(-8)= 1/8.

Personally, I think its a bad choice of words.

I agree that the words are confusing. Gradient of the normal suggests an operation on a vector that is supposed to be an operation on a scalar. Your interpretation that it means the slope of something seems to be the only reasonable interpretation, but I found myself asking what it was the slope of, and finally arrived at the conclusion that was probably obvious to you that it is the slope of the normal to the curve y vs x along a line of constant z through the point (3,-2,f(3,-2)) = (3,-2,-9). The slope of that curve can also be found by implicit differentiation of f(x,y) = constant to find the slope at (3,-2), where y' = -8, so of course the slope of the normal through that point is 1/8.

But since the title of this thread is "tangent plane" I thought it would be nice to connect this result to finding the plane tangent to the surface defined by f(x,y). The slope of the normal to the curve y vs x for constant z gives the slope of the projection of the vector normal to the surface onto the plane of constant z, but what does the tangent plane and the full normal vector look like? The procedure for finding the complete normal vector and the equation of the tangent plane is nicely outlined in the link posted by whozum (except for repeated references to the "tangent line" that I think should say "tangent plane"; I dropped a note to the author about that). The result is that the normal vector has components (-8,-1,-1), and the equation of the plane is

[tex]z = - 8x - y + 13[/tex]

The figure shows the original function and the tangent plane in a 15x15x15 cube surrounding the point (3,-2,-9). To the left is a zoomed in view of the surface cut by the surface of constant z = -9.
 

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FAQ: Calculating the Gradient of f(x,y): A Step-by-Step Walkthrough

What is the gradient of a function?

The gradient of a function is a vector that shows the direction and magnitude of the steepest increase of the function at any given point. It is represented by the symbol ∇f(x,y) and is found by taking the partial derivatives of the function with respect to each variable.

Why is calculating the gradient important?

Calculating the gradient is important because it helps us understand the behavior of a function at a specific point. It also allows us to find the direction of fastest increase of the function, which is useful in optimization problems. Additionally, the gradient is a key concept in multivariable calculus and is used in many fields of science, including physics, engineering, and economics.

What is the process for calculating the gradient of a function?

The process for calculating the gradient of a function involves taking the partial derivatives of the function with respect to each variable and then combining them into a vector using the ∇ symbol. This vector represents the gradient of the function and can be evaluated at any given point by plugging in the corresponding values for x and y.

How do you interpret the components of the gradient vector?

The components of the gradient vector represent the rates of change of the function with respect to each variable. The magnitude of the vector represents the steepness of the function at a specific point, with larger magnitudes indicating steeper increases. The direction of the vector represents the direction of the steepest increase of the function, with the positive x-component pointing in the direction of increasing x and the positive y-component pointing in the direction of increasing y.

Can the gradient of a function be negative?

Yes, the gradient of a function can be negative. This means that the function is decreasing in the direction of the gradient vector. However, the magnitude of the gradient vector will still represent the steepest decrease of the function at that point. It is important to note that the gradient can be negative in one component and positive in the other, depending on the behavior of the function in each direction.

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