Why does the gradient vector point straight outward from a graph?

In summary, the gradient vector measures the change and direction of a scalar field and points in the direction of the greatest rate of increase. This is expressed in terms of unit vectors, and locally makes an angle of 90 degrees with the graph or surface at a particular point. This can also be understood as the directional derivative being 0 in any direction tangent to the surface, making the gradient vector perpendicular to the surface.
  • #1
gikiian
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A gradient vector points out of a graph (or a surface in 3D case). Locally, it makes an angle of 90 degrees with the graph at a particular point. Why is that so?

Thanks.
 
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  • #2
Hi.

The gradient vector measures the change and direction of a scalar field. The direction of the gradient is expressed in terms of unit vectors (in 3-dimensions, say) and points in the direction of the greatest rate of increase of the scalar field, and whose magnitude is the greatest rate of change.
 
  • #3
Another way of looking at it is that the "directional derivative", the rate of change of function f(x,y,z) as you move in the direction of unit vector [itex]\vec{v}[/itex], is given by [itex]\nabla f\cdot\vec{v}[/itex]. If the function is given implicitely by f(x,y,z)= 0 (or any constant, then on the surface f is a constant and so it derivative is 0 in any direction tangent to surface: the dot product of [itex]\nabla f\cdot \vec{v}[/itex], with [itex]\vec{v}[/itex] tangent to the surface, is 0 so [itex]\nabla f[/itex] is perpendicular to the surface.
 

1. Why does the gradient vector point outward from a graph?

The gradient vector points outward from a graph because it represents the direction of the steepest increase in a function. This means that at any given point on the graph, the gradient vector will point in the direction of the greatest increase in the function's output.

2. How is the gradient vector calculated?

The gradient vector is calculated by taking the partial derivatives of a multivariable function with respect to each of its variables. These partial derivatives are then combined to form a vector that represents the direction and magnitude of the function's steepest increase.

3. Does the gradient vector always point outward?

No, the gradient vector does not always point outward. It only points outward when the function is increasing in the direction of the vector. If the function is decreasing in the direction of the vector, then the gradient vector will point inward.

4. Can the gradient vector point in more than one direction?

Yes, the gradient vector can point in more than one direction. This can occur when the function has multiple local maximum or minimum points. In these cases, the gradient vector will point in the direction of the steepest increase or decrease at each point.

5. How is the gradient vector used in real-world applications?

The gradient vector is used in many real-world applications, particularly in fields such as physics, engineering, and economics. It is used to optimize functions and find the most efficient or optimal solutions. For example, in physics, the gradient vector can be used to determine the path of a moving object in a gravitational field or the direction of the electric field at a particular point.

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