What is the Gradient of a Function at a Given Point?

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In summary, the person is trying to find the direction of steepest ascent of the function f(x) = x^2 - 4y^2 - 9 at the given point (1,-2). They believe they can use the gradient to determine this, but when they input their equation into Wolfram Alpha, it interprets it incorrectly and does not provide a direction. The person is asking for help with finding the correct input and understanding how to take the gradient.
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JProgrammer
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I am trying to find the direction of steepest ascent of this function with this given point:

f(x) = x^2 - 4y^2 - 9

(1,-2)

I have the understanding that the steepest ascent or in some cases descent can be measured by the gradient. So in wolfram alpha I type in: gradient f(x) = x^2 - 4y^2 - 9, (1,-2) it says it interprets my input as: grad(-9+x^2-4 y^2, 18+x^2-4 y^2)
and gives me: grad(-9+x^2-4 y^2, 18+x^2-4 y^2) = ({2 x, 2 x}, {-8 y, -8 y}).

It interprets my input wrong and does not give me a direction. If someone could tell me what I am doing wrong and what I need to do instead, I would appreciate.

Thank you.
 
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Shouldn't that be f(x, y)?
 
  • #3
JProgrammer said:
I am trying to find the direction of steepest ascent of this function with this given point:

f(x) = x^2 - 4y^2 - 9

(1,-2)

I have the understanding that the steepest ascent or in some cases descent can be measured by the gradient. So in wolfram alpha I type in: gradient f(x) = x^2 - 4y^2 - 9, (1,-2) it says it interprets my input as: grad(-9+x^2-4 y^2, 18+x^2-4 y^2)
and gives me: grad(-9+x^2-4 y^2, 18+x^2-4 y^2) = ({2 x, 2 x}, {-8 y, -8 y}).

It interprets my input wrong and does not give me a direction. If someone could tell me what I am doing wrong and what I need to do instead, I would appreciate.

Thank you.
Really? I got this. But it's so simple a problem, why are you using W|A to do it? Do you know how to take the gradient?

-Dan
 

FAQ: What is the Gradient of a Function at a Given Point?

What is the direction of steepest ascent?

The direction of steepest ascent is the direction in which a function increases the fastest. It is the direction of the gradient vector, which is a vector that points in the direction of the greatest increase of a function at a given point.

How is the direction of steepest ascent calculated?

The direction of steepest ascent is calculated by finding the gradient vector of a function at a given point and normalizing it to a unit vector. This unit vector represents the direction of steepest ascent.

What is the significance of the direction of steepest ascent?

The direction of steepest ascent is important in optimization problems, as it can help determine the fastest way to reach a maximum point on a function. It is also useful in gradient descent algorithms, where the direction of steepest descent is used to find the minimum of a function.

Can the direction of steepest ascent change at different points on a function?

Yes, the direction of steepest ascent can change at different points on a function, as it is dependent on the gradient vector, which can vary from point to point. However, at a local maximum point, the direction of steepest ascent will be zero as there is no increase in the function in any direction.

How is the direction of steepest ascent related to the rate of change of a function?

The direction of steepest ascent is directly related to the rate of change of a function. The gradient vector, which represents the direction of steepest ascent, is also the vector that points in the direction of the greatest rate of change of the function. This means that the direction of steepest ascent is the direction in which the function is changing the fastest.

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