How to solve gradient with ln?

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In summary, the conversation discusses the process of solving the gradient of a function when the function is ln |r| and 1/r. The conversation includes attempts at using the chain rule and finding the partial derivatives, and ultimately concludes that the gradient of ln |r| is -(r/r^2) and the gradient of 1/r is -(r/r^3).
  • #1
Logan Land
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To solve the gradient f when f = ln |r| do I start with differentiating each x,y,z term of the vector?Like ln|x| ln|y|...etc.

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  • #2
(ATTEMPT)

gradient(ln||^r||)

=((d/dx)x*+(d/dy)y*+(d/dz)z*)(ln||r||)
=(x/r^2)x*+(y/r^2)y*+(z/r^2)z*
=(x,y,z)/r^2
=(^r)/r^2

correct?
 
  • #3
Notice that $\ln(\| {\mathbf r} \|) = \ln (\sqrt{x^2+y^2+z^2})$. You need to use the chain rule for the gradient. I don't understand your notation. :(
 
  • #4
Fantini said:
Notice that $\ln(\| {\mathbf r} \|) = \ln (\sqrt{x^2+y^2+z^2})$. You need to use the chain rule for the gradient. I don't understand your notation. :(
i tried to follow how I attempted the next question, gradient f if f=1/r
(d/dx)xbar+(d/dy)ybar+(d/dz)zbar * (1/r)
(-x/r^3)xbar+(-y/r^3)ybar+(-z/r^3)zbar
= -(x,y,z)/r^3
=-rbar/r^3thats how I tried but I guess was wrong?
 
  • #5
d/dx ln(sqrt x^2+y^2+z^2) = x/(x^2+y^2+z^2) d/dy = y/(x^2+y^2+z^2) and d/dz = z/(x^2+y^2+z^2)
so then would it become (x+y+z)/(x^2+y^2+z^2)? which is r*/r^2?
 
  • #6
Remember the gradient is $$\nabla f = \left( \frac{\partial f}{\partial x}, \frac{\partial f}{\partial y}, \frac{\partial f}{\partial z} \right).$$ Since we've established $$\frac{\partial f}{\partial x} = \frac{x}{\sqrt{x^2+y^2+z^2}}$$ and likewise for the other partial derivatives, we have $$\nabla f = \frac{(x,y,z)}{\sqrt{x^2+y^2+z^2}} = \frac{{\mathbf r}}{r} = \frac{\widehat{ {\mathbf r}}}{r^2}.$$ :) Hope this helps. What do you mean with 'zbar'?
 
  • #7
Fantini said:
Remember the gradient is $$\nabla f = \left( \frac{\partial f}{\partial x}, \frac{\partial f}{\partial y}, \frac{\partial f}{\partial z} \right).$$ Since we've established $$\frac{\partial f}{\partial x} = \frac{x}{\sqrt{x^2+y^2+z^2}}$$ and likewise for the other partial derivatives, we have $$\nabla f = \frac{(x,y,z)}{\sqrt{x^2+y^2+z^2}} = \frac{{\mathbf r}}{r} = \frac{\widehat{ {\mathbf r}}}{r^2}.$$ :) Hope this helps. What do you mean with 'zbar'?

I did not understand. gradiente (1/r) = - r/ r^3 ??
 
  • #8
Patricio Lima said:
I did not understand. gradiente (1/r) = - r/ r^3 ??

Hi Patricio Lima, welcome to MHB, (Wave)

We had here that:
$$\operatorname{grad} \ln\|\mathbf r\| = -\frac{\mathbf r}{r^2}$$
Similarly we can find:
$$\pd {}x \frac 1r = -\frac 1{r^2}\pd r x = -\frac 1{r^2}\pd {} x\sqrt{x^2+y^2+z^2}
= -\frac 1{r^2}\cdot\frac{2x}{2\sqrt{x^2+y^2+z^2}}
= -\frac x{r^3}$$
So that:
$$\operatorname{grad} \frac 1r = -\frac{\mathbf r}{r^3}$$
(Thinking)
 

Related to How to solve gradient with ln?

What is the formula for solving gradient with ln?

The formula for solving gradient with ln is:∇f(x,y) = (∂f/∂x, ∂f/∂y) = (1/x, 1/y)

Can I use the chain rule when solving gradient with ln?

Yes, the chain rule can be used when solving gradient with ln. The derivative of ln(x) is 1/x, and the chain rule allows us to find the derivative of a composite function.

Do I need to use logarithmic differentiation when solving gradient with ln?

No, logarithmic differentiation is not necessary when solving gradient with ln. The derivative of ln(x) can be found using the chain rule and the properties of logarithms.

What is the relationship between the natural logarithm and the gradient?

The natural logarithm, ln(x), is the inverse of the exponential function, e^x. The gradient represents the rate of change of a function at a given point, and the natural logarithm can be used to find the gradient of exponential functions.

Are there any common mistakes made when solving gradient with ln?

One common mistake is forgetting to apply the chain rule when finding the derivative of a function involving ln(x). It is also important to remember the properties of logarithms, such as ln(ab) = ln(a) + ln(b) and ln(a/b) = ln(a) - ln(b).

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