Gradient vectors and level surfaces

In summary: The tangent plane is orthogonal to the gradient.In summary, the gradient vector is orthogonal to the level surface at a given point, as shown by the equation ##\nabla f \cdot \vec{\dot x} = 0##. The tangent vector lies within the level surface and the tangent plane is orthogonal to the gradient.
  • #1
Haku
30
1
TL;DR Summary
I know that the gradient vector is orthogonal to the level surface at some point p, but is the gradient vector also orthogonal to the tangent vector at that point? Or is there some other relationship between the tangent vector and level surface?
Homework Statement:: Wondering about the relationship between gradient vectors, level surfaces and tangent planes
Relevant Equations:: .

I know that the gradient vector is orthogonal to the level surface at some point p, but is the gradient vector also orthogonal to the tangent vector at that point? Or is there some other relationship between the tangent vector and level surface?
 
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  • #2
I know that the gradient vector is orthogonal to the level surface at some point p, but is the gradient vector also orthogonal to the tangent vector at that point? Or is there some other relationship between the tangent vector and level surface?
 
  • #3
Haku said:
Homework Statement:: Wondering about the relationship between gradient vectors, level surfaces and tangent planes
Relevant Equations:: no equations

I know that the gradient vector is orthogonal to the level surface at some point p, but is the gradient vector also orthogonal to the tangent vector at that point?
Yes.
Consider ##w = f(x(t), y(t), z(t))##.
If w is held constant, you get a level surface.
Differentiation with respect to t yields ##\frac{\partial f}{\partial x} \frac{dx}{dt} + \frac{\partial f}{\partial y} \frac{dy}{dt} + \frac{\partial f}{\partial z} \frac{dz}{dt} = 0##
The above can be rewritten as ##\nabla f \cdot \vec{\dot x} = 0##, which shows that the gradient of f is orthogonal to the tangent vector. Here ##\vec{\dot x}## is the vector ##(\frac{dx}{dt}, \frac{dy}{dt}, \frac{dz}{dt})##.
Haku said:
Or is there some other relationship between the tangent vector and level surface?
 
  • #4
The tangent vector in in the level surface.
 

FAQ: Gradient vectors and level surfaces

What is a gradient vector?

A gradient vector is a mathematical concept used in multivariable calculus to represent the direction and magnitude of the steepest ascent of a function at a given point. It is a vector that points in the direction of the greatest increase of the function and its magnitude represents the rate of change of the function in that direction.

How are gradient vectors and level surfaces related?

Gradient vectors and level surfaces are closely related as the gradient vector is always perpendicular to the level surface at any given point. This means that the gradient vector points in the direction of the steepest ascent, while the level surface represents all points with the same function value. Therefore, the gradient vector is tangent to the level surface at the point where they intersect.

How do you find the gradient vector of a function?

The gradient vector of a function can be found by taking the partial derivatives of the function with respect to each variable and combining them into a vector. For example, if the function is f(x,y,z), the gradient vector would be [∂f/∂x, ∂f/∂y, ∂f/∂z]. This vector can then be used to find the direction and magnitude of the steepest ascent at any given point.

What is the significance of gradient vectors and level surfaces in real-world applications?

Gradient vectors and level surfaces have many practical applications in fields such as physics, engineering, and economics. They can be used to optimize processes, such as finding the most efficient route for a delivery truck, or to determine the direction and rate of change of a physical quantity, such as temperature or pressure.

Can gradient vectors and level surfaces be used to solve optimization problems?

Yes, gradient vectors and level surfaces are often used in optimization problems to find the minimum or maximum value of a function. By setting the gradient vector equal to zero, the critical points of the function can be found, which can then be used to determine the optimal solution.

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