Regarding gradient vector

In summary, a gradient vector is a vector that represents the direction and magnitude of the steepest increase or decrease of a function at a given point. It is calculated by taking the partial derivatives of a multivariable function and combining them into a vector. The gradient vector is significant in understanding the behavior of a function and can have both positive and negative components. In machine learning, it is used to update model parameters and minimize error functions.
  • #1
seshikanth
20
0
As we know grad F (F surface) is in normal direction. But we also have (grad F(r)) x r = F'(r) (r) x r = 0
this implies grad F is in direction of r i.e., radial direction. Radial and normal directions need not be same. Can any öne clarify THE DIRECTION OF GRAD VECTOR?
 
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  • #2
Gentle reminder
 
  • #3
In your second formula, you refer to "F(r)" so you are assuming that F depends only on r and so is spherically symmetric. The first formula does not assume that .
 
  • #4
Got it! Thanks!
 
  • #5


The direction of the gradient vector is dependent on the direction of the variable in question. In the case of a scalar field, the gradient vector points in the direction of the steepest increase of the function. This can be thought of as the direction in which the function is changing the most rapidly.

In the example given, the gradient vector is in the normal direction because the function is a surface, and the normal direction is perpendicular to the surface. However, when considering the gradient vector in terms of the variable r, it is in the radial direction because r is a distance from the origin and the gradient vector is pointing towards the direction of greatest increase in the function from that point.

Therefore, the direction of the gradient vector can vary depending on the context and should be interpreted accordingly. It is important to consider the variable in question and the overall context when determining the direction of the gradient vector.
 

1. What is a gradient vector?

A gradient vector is a mathematical concept that represents the direction and magnitude of the steepest increase or decrease of a function at a given point. It is a vector that points in the direction of maximum change and its length represents the rate of change.

2. How is a gradient vector calculated?

A gradient vector is calculated by taking the partial derivatives of a multivariable function with respect to each of its variables and combining them into a vector. The resulting vector is the gradient vector.

3. What is the significance of the gradient vector?

The gradient vector is significant because it helps us understand the behavior of a function at a specific point. It tells us the direction in which the function is changing the fastest and the rate at which it is changing. This information is useful in optimization problems and in understanding the behavior of physical systems.

4. Can a gradient vector be negative?

Yes, a gradient vector can have negative components. This indicates a decrease in the function's value in that direction. However, the magnitude of the gradient vector is always positive, as it represents the rate of change.

5. How is a gradient vector used in machine learning?

In machine learning, the gradient vector is used to update the parameters of a model in the direction of steepest descent. This helps the model learn from data and improve its performance. It is also used in gradient descent algorithms to minimize the error or loss function of the model.

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