Lost differentiating a function

In summary, the conversation was about solving a non-linear problem and understanding a specific method from a PDF. The method involved differentiating a function with respect to a parameter and using the chain rule. The conversation ended with the understanding that the final result was a dot product between the gradient of the function and the parameter vector.
  • #1
o_damhsa
2
0

Homework Statement

Homework Statement [/b]
Hello. I'm trying to solve a non-linear problem and I have been working through the notes on this pdf to try and understand the method before I use it but I get stuck at one of the steps. The pdf is here:
http://www.nrbook.com/a/bookfpdf/f9-7.pdf
I cannot follow how the author went from equation 9.7.8 to 9.7.9



Homework Equations


The author has the following equation:

[tex]g (\lambda) \equiv f(xold + \lambda p) [/tex]
He differentiates this function with respect to [tex] \lambda [/tex] and gets the following
[tex]g'(\lambda) = \nabla f \cdot p[/tex]


The Attempt at a Solution


My understanding is that the author differentiated with respect to [tex] \lambda[/tex] so they got the [tex]\nabla f[/tex] and then differentiated what was inside the function to get the [tex]p[/tex] value. But I don't see how it became a dot product, or am I just misreading it?
 
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  • #2
Welcome to PF, o_damhsa


The pdf doesn't open for me, but here's how it looks from what you've said.

Call [tex]xold=\langle x_1,x_2,x_3\rangle[/tex] and [tex]p=\langle p_1,p_2,p_3 \rangle[/tex]

Then [tex]g(\lambda)=f(x_1+\lambda p_1,x_2+\lambda p_2,x_3+\lambda p_3)[/tex]

Now by the Chain Rule,

[tex]\frac{dg}{d\lambda}=
\frac{\partial f}{\partial x}\frac{dx}{d\lambda}
+\frac{\partial f}{\partial y}\frac{dy}{d\lambda}
+\frac{\partial f}{\partial z}\frac{dz}{d\lambda} [/tex]

[tex]\frac{dg}{d\lambda}=
\frac{\partial f}{\partial x} p_1
+\frac{\partial f}{\partial y} p_2
+\frac{\partial f}{\partial z} p_3[/tex]

[tex]\frac{dg}{d\lambda}=
\left\langle \frac{\partial f}{\partial x},\frac{\partial f}{\partial y},\frac{\partial f}{\partial z}\right\rangle\cdot\langle p_1,p_2,p_3 \rangle [/tex]


[tex]\frac{dg}{d\lambda}=
\nabla f\cdot p [/tex]
 
  • #3
Hello Billy Bob,

Thank you very much for your reply! It makes perfect sense now :smile:

o_damhsa
 

1. What is "Lost differentiating a function"?

Lost differentiating a function refers to the process of finding the derivative of a function when the function is not explicitly given, but only a set of data points is provided.

2. Why is "Lost differentiating a function" important?

Lost differentiating a function is important because it allows us to find the rate of change of a function at any given point, even when the function is not explicitly known. This is useful in many real-world applications, such as economics, physics, and engineering.

3. What are the methods used for "Lost differentiating a function"?

The two main methods used for lost differentiating a function are the finite difference method and the interpolation method. The finite difference method involves using the difference between two data points to approximate the derivative, while the interpolation method involves fitting a curve to the data points and then finding the derivative of the curve.

4. What are the challenges of "Lost differentiating a function"?

One of the main challenges of lost differentiating a function is the potential for errors or inaccuracies due to the finite number of data points available. Another challenge is the choice of method, as some methods may be more suitable for certain types of data or functions.

5. How can "Lost differentiating a function" be applied in real-world situations?

Lost differentiating a function can be applied in many real-world situations, such as calculating the velocity or acceleration of an object based on position data, estimating the demand curve for a product based on sales data, or determining the rate of change of a stock price based on historical data. It is also widely used in data analysis and machine learning applications.

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