Question about the gradient of a function

  • Context: Undergrad 
  • Thread starter Thread starter pamparana
  • Start date Start date
  • Tags Tags
    Function Gradient
Click For Summary
SUMMARY

The gradient of a scalar field is defined as a vector that points in the direction of the greatest increase of that field. The relationship between the gradient and the directional derivative is established through the equation D_{\hat u}f(P) = \nabla f (P)·\hat u, which indicates that the maximum rate of change occurs when the direction of the unit vector u aligns with the gradient vector. This conclusion is supported by the fact that the cosine of the angle between the gradient and the direction vector is maximized at zero degrees, confirming that the gradient indeed points towards the direction of maximum increase.

PREREQUISITES
  • Understanding of scalar fields and their properties
  • Familiarity with vector calculus concepts
  • Knowledge of partial derivatives and their computation
  • Basic grasp of directional derivatives and their significance
NEXT STEPS
  • Study the properties of scalar fields in advanced calculus
  • Learn about the application of gradients in optimization problems
  • Explore the concept of directional derivatives in greater detail
  • Investigate the implications of gradients in multivariable functions
USEFUL FOR

Students and professionals in mathematics, physics, and engineering who are looking to deepen their understanding of vector calculus and its applications in analyzing scalar fields and optimization problems.

pamparana
Messages
123
Reaction score
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
 
Last edited by a moderator:
Physics news on Phys.org
Think about the directional derivative of f at a point P in the direction of a unit vector u:

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

where \theta 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 \cos\theta = 1, which is when \theta= 0 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.
 
Ah! That makes sense. Many thanks for the explanation. Much appreciated.

Luc
 

Similar threads

  • · Replies 18 ·
Replies
18
Views
3K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 16 ·
Replies
16
Views
4K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 8 ·
Replies
8
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K