Multivariable Calculus: Applications of Grad (and the Chain Rule?)

gadje
Messages
23
Reaction score
0

Homework Statement


We say that a differentiable function f : \mathbb{R}^n \rightarrow \mathbb{R} is homogenous of degree p if, for every \mathbf{x} \in \mathbb{R}^n and every a>0,
f(a\mathbf{x}) = a^pf(\mathbf{x}).

Show that, if f is homogenous, then \mathbf{x} \cdot \nabla f(\mathbf{x}) = p f(\mathbf{x}) .

Homework Equations


The chain rule (not sure if I need it): \displaystyle \frac{d}{dt} f(\mathbf{x}(t)) = \Sigma_{i = 1}^{n} f_{x_i}\dot{x_i} = \dot{\mathbf{x}} \cdot \nabla f

The Attempt at a Solution



Well, I see the resemblance between the rightmost hand side of the chain rule I wrote down, but I don't really understand how the chain rule is applied in this situation, seeing as there isn't anything about x being a function of something else here.

Any ideas?
Cheers.
 
Last edited:
Physics news on Phys.org
Try differentiating

f(a\mathbf{x}) = a^pf(\mathbf{x})

with respect to a.
 
\frac{\partial}{\partial a} f(a \mathbf{x}) = pa^{p-1}f(\mathbf{x})

Okay. I'm still clueless.

EDIT:

Hang on. \frac{\partial}{\partial a} f(a \mathbf{x}) = \frac{\partial}{\partial a} (a \mathbf{x}) \frac{\partial}{\partial \mathbf{x}} f(a\mathbf{x}) = \mathbf{x} \cdot \nabla f (a \mathbf{x}) (if you'll forgive the abuse of notation), right?
 
Last edited:
Good. Now let a=1.
 
Thread 'Use greedy vertex coloring algorithm to prove the upper bound of χ'
Hi! I am struggling with the exercise I mentioned under "Homework statement". The exercise is about a specific "greedy vertex coloring algorithm". One definition (which matches what my book uses) can be found here: https://people.cs.uchicago.edu/~laci/HANDOUTS/greedycoloring.pdf Here is also a screenshot of the relevant parts of the linked PDF, i.e. the def. of the algorithm: Sadly I don't have much to show as far as a solution attempt goes, as I am stuck on how to proceed. I thought...
Back
Top