Help with Gradient-related concepts

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In summary, the conversation discusses the concept of a Gradient vector and its application to real-valued functions. It is explained that for a smooth function with a real number in its range, the rate of change in the direction of a unit tangent vector is 0. This is extended to functions in three variables and further sources are suggested for visualizing this concept. The conversation concludes with a clarification on the understanding of the concept.
  • #1
Ryuzaki
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I'm trying to understand the concept of a Gradient vector, and it seems I'm having trouble visualizing certain stuff. So, I was hoping if someone could resolve some of the questions I'm having on my mind.

Okay, so I'm considering a real-valued function [itex] z = f(x,y) [/itex] which is smooth, i.e., its partial derivatives with respect to [itex] x [/itex] and [itex] y [/itex] exist and are continuous. Let [itex] k [/itex] be a real number in the range of [itex] f [/itex] and let [itex] \mathbf v [/itex] be a unit vector in ℝ2, which is tangent to the level curve [itex] f(x,y) = k [/itex].

Now, I am told to understand that the rate of change of [itex] f [/itex] in the direction of [itex]\mathbf v [/itex] is [itex] 0 [/itex], i.e., Dvf [itex]= 0[/itex]. And the explanation for it, in almost all of the sources I've seen is, that it's because [itex] \mathbf v [/itex] is a tangent vector to this curve.

1. Can anyone tell me how [itex] \mathbf v [/itex] being simply a tangent vector implies this?

2. Is this concept simply extended to the case of a function in three variables, by level surfaces and tangent planes? In this case, wouldn't there be an infinite number of tangent vectors, and thus an infinite number of gradient vectors (since the gradient vector is perpendicular to the tangent vector, i.e., normal to the surface). Can anyone point out any sources that help in visualizing this?

Thank you!
 
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  • #2
We might as well do it for functions of ##n## variables since the argument is the exact same; note that the argument I'm giving here is not exactly rigorous but hopefully it is satisfying enough. Let ##f:\mathbb{R}^{n}\rightarrow \mathbb{R}## be a smooth scalar field and let ##M = f^{-1}(c), c\in f(\mathbb{R}^{n})## be a level set of ##f##. Let ##p\in M## and ##v## be any vector tangent to ##M## at ##p##, and choose a smooth curve ##\gamma :(-\epsilon,\epsilon)\rightarrow M## with ##\gamma(0) = p, \dot{\gamma}(0) = v##. Note that ##f(\gamma(t)) = c = \text{const.}## identically, since the image of the curve lies in the level set, so ##\frac{\mathrm{d} }{\mathrm{d} t}(f(\gamma(t)))|_{t=0} = \nabla f(\gamma(0))\cdot \dot{\gamma}(0) = \nabla f(p)\cdot v = 0## where I have used the chain rule in the very first step. Since the chosen point and tangent vector were entirely arbitrary, ##\nabla f## will be perpendicular to all vectors tangent to ##M## at each point of ##M##.
 
  • #3
Thank you, WannabeNewton! I think I understand it now. :smile:
 
  • #4
Ryuzaki said:
Thank you, WannabeNewton! I think I understand it now. :smile:
Let me know if you have more questions. Also, work it out specifically for the case where ##f(x,y,z) = x^2 + y^2 + z^2## i.e. when the level sets are spheres, ##x^2 + y^2 + z^2 = k##. Take the gradient and hopefully you can picture the outward (outward from the origin) radial vector field that results and see how it is perpendicular to all the spheres centered at the origin with different radii given by different values of ##k##.
 

FAQ: Help with Gradient-related concepts

What is a gradient?

A gradient is a mathematical concept used in calculus and vector analysis. It is a vector that represents the rate of change of a function with respect to its variables, and it points in the direction of the greatest increase of the function.

How is a gradient calculated?

The gradient 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 represents the slope of the function in each direction.

What is the relationship between a gradient and a slope?

A gradient and a slope have a similar concept, but they are not the same. A gradient represents the slope of a function in multiple dimensions, while a slope is the change in the y-value divided by the change in the x-value of a line on a graph.

What are some real-life applications of gradients?

Gradients have many applications in various fields such as physics, engineering, economics, and computer science. Some examples include calculating the flow of heat or electricity, optimizing routes for transportation, and training neural networks in machine learning.

How can I use gradients to optimize a function?

By using the gradient, it is possible to find the direction of greatest increase of a function. This information can be used to find the minimum or maximum value of a function and to optimize its performance. This is commonly used in optimization problems in mathematics and computer science.

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