Question about the gradient of a function

In summary, the gradient of a scalar field points in the direction of the greatest increase in the field. This can be seen through the calculation of the directional derivative, which is maximized when the unit vector is pointed in the direction of the gradient. This shows that the gradient must point in the direction of the maximum rate of change at a specific point.
  • #1
pamparana
128
0
Hello everyone,

This might be a bit of a silly question. Just looking at the definition of a gradient of a scalar field in wikipedia:

http://en.wikipedia.org/wiki/Gradient"

So, the gradient points in the direction of the greatest increase in scalar field.

From the definition with the partial derivatives, it is not clear to me why that vector should point in the direction of greatest increase though. I understand how one computes the gradient but from that definition how can one conclude that this will point in the direction of greatest change?

Many thanks for any help you can give me.

Cheers,
Luc
 
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  • #2
Think about the directional derivative of f at a point P in the direction of a unit vector u:

[tex]D_{\hat u}f(P) = \nabla f (P)\cdot \hat u = |\nabla f(P)| |\hat u|\cos(\theta)= |\nabla f(P)| \cos(\theta)[/tex]

where [itex]\theta[/itex] is the angle between u and the gradient. Notice that the only variable in this equation is the direction of u. The directional derivative will be max when [itex]\cos\theta[/itex] = 1, which is when [itex]\theta= 0[/itex] which says u is pointed in the direction of the gradient. So the gradient must point in the direction of max rate of change at P.
 
  • #3
Ah! That makes sense. Many thanks for the explanation. Much appreciated.

Luc
 

Related to Question about the gradient of a function

1. What is the gradient of a function?

The gradient of a function is a vector that represents the rate of change of the function at a particular point. It shows the direction and magnitude of the steepest slope at that point.

2. How is the gradient of a function calculated?

The gradient of a function is calculated by taking the partial derivatives of the function with respect to each of its variables and combining them into a vector. This vector is also known as the gradient vector.

3. What is the significance of the gradient of a function?

The gradient of a function is significant because it can be used to find the maximum and minimum values of the function. It also helps in understanding the behavior of the function and determining the direction of steepest ascent or descent.

4. 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 that direction and the rate of change is negative. The magnitude of the gradient vector represents the steepness of the slope, regardless of its direction.

5. How is the gradient of a function used in real life?

The concept of gradient is used in many fields such as physics, engineering, economics, and machine learning. In physics, it is used to calculate the force and direction of a field. In economics, it is used to optimize production and minimize costs. In machine learning, it is used to update the parameters of a model and improve its performance.

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