Derivative laws question numerical analysis

In summary, the conversation discusses the derivative of a function that is the product of multiple functions. The result is a sum of terms, each being the product of multiple functions. The notation used for this is \prod for products and \sum for sums. The difference between upper-case pi and lower-case pi is also mentioned.
  • #1
nhrock3
415
0
23ra8u0.jpg


i know the law (fg)'=f'g+fg'
but here there is epsilon
and it turns to someother sign i don't know what it says.

i can't understand the transition
?
 
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  • #2
nhrock3 said:
23ra8u0.jpg


i know the law (fg)'=f'g+fg'
but here there is epsilon
and it turns to someother sign i don't know what it says.

i can't understand the transition
?
There's no epsilon in what you posted. Are you talking about [itex]\prod[/itex]? Note that this is upper-case pi, which is different from the constant [itex]\pi[/itex], lower-case pi.

Upper-case pi is similar to upper-case sigma [itex]\sigma[/itex], except that it is used for products rather than sums. here's an example using the product notation.
[tex]\prod_{m = 1}^5 m = 1 \cdot 2 \cdot 3 \cdot 4 \cdot 5 = 5! = 120[/tex]
 
  • #3
ok but why the derivative change epsilon to pi?
 
  • #4
What are you talking about? There is no epsilon.
 
  • #5
With epsilon do you mean the summation sign? If so that one is called capital sigma.

Taking the derivative of a function that is the product of two other functions yields two terms as you know, (fg)'=f'g+g'f. In our case we don't have a function consisting of the product of two functions but of a product of m functions.

[tex]
\prod_{j=0}^m f_j=f_0f_1f_2 \cdot ... \cdot f_m
[/tex]

Now taking the derivative of that product yields:

[tex]
\frac{d}{dx}\prod_{j=0}^m=\frac{d}{dx}(f_0f_1f_2 \cdot ... \cdot f_m)=f'_0f_1f_2 \cdot ... \cdot f_m+f_0f_1'f_2 \cdot ... \cdot f_m+...+f_0f_1f_2 \cdot ... \cdot f_m'
[/tex]

You can see that we get m terms that are added together and every term is the product of m terms. We can write this compactly as:

[tex]
\sum_{i=0}^m \frac{df_i}{dx}\prod_{\substack{ j=0 \\ j \neq i}}^m f_j
[/tex].
 
Last edited:

1. What are the basic derivative laws in numerical analysis?

The basic derivative laws in numerical analysis include the power rule, product rule, quotient rule, chain rule, and the derivative of a constant. These laws are used to find the derivative of a function at a specific point.

2. How do these derivative laws apply to numerical analysis?

The derivative laws in numerical analysis are used to approximate the derivative of a function at a specific point. This is useful in many applications, such as optimization problems or predicting the behavior of a system.

3. Can these derivative laws be used for all types of functions?

Yes, these laws can be used for all types of functions, including polynomial, exponential, logarithmic, and trigonometric functions. However, some functions may require additional techniques or approximations to find their derivatives.

4. How do errors in numerical analysis affect the accuracy of derivative calculations?

Numerical methods for finding derivatives can introduce errors due to rounding and truncation. These errors can accumulate and affect the accuracy of the final result. It is important to use proper techniques and take into account the precision of the numbers being used to minimize these errors.

5. Are there any limitations to using derivative laws in numerical analysis?

While these laws are powerful tools for finding derivatives, they do have some limitations. They may not work for all functions, and they may not provide an exact solution. Additionally, they may not be able to handle discontinuous functions or functions with sharp turns. In these cases, other numerical methods may be necessary.

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