Query on indexing to determine coefficients

In summary, the author is discussing an expression for the matrix element B_ij and states that it is not valid for certain values of i and j. However, he goes on to calculate B_11, B_1j, and B_j1, which seem to contradict his statement. The author also notes an editing error in the expression. The questioner asks for clarification and mentions a related question about the choice of approximation functions affecting the outcome.
  • #1
bugatti79
794
1
Folks,
I am interested to know what the author is doing in the following

##\displaystyle B_{ij}=EL ij (L)^{(i+j-1)} \left[ \frac{(i-1)(j-1)}{i+j-3} -\frac{2(ij-1)}{i+j-2}+\frac{(i+1)(j+1)}{i+j-1}\right]##

he states that this expression is not valid for ##B_{ij}## when ##i=1## and ##j=1,2,...N##

...yet he goes on to actually calculate

##B_{11}=4EIL##, ##B_{1j}=B_{j1}=2EIL^j##, ##(j>1)##

I understand the the numerator in the first 2 terms inside the big brakets are both 0 when i=j=1 but we still yield a value from the third term...
Any insight will be appreciated
Regards

PS:I notice there is some editing problem with the 3 terms inside the big brackets. There should be a minus and plus separating the terms.
 
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  • #2
bugatti79 said:
Folks,
I am interested to know what the author is doing in the following

##\displaystyle B_{ij}=EL ij (L)^{(i+j-1)} \left[ \frac{(i-1)(j-1)}{i+j-3} -\frac{2(ij-1)}{i+j-2}+\frac{(i+1)(j+1)}{i+j-1}\right]##

he states that this expression is not valid for ##B_{ij}## when ##i=1## and ##j=1,2,...N##
That's not at all obvious. The only restrictions I see are that
1. i + j ≠ 3 (would make the first denominator vanish)
2. i + j ≠ 2 (would make the second denominator vanish)
3. i + j ≠ 1 (would make the third denominator vanish)
bugatti79 said:
...yet he goes on to actually calculate

##B_{11}=4EIL##, ##B_{1j}=B_{j1}=2EIL^j##, ##(j>1)##
I don't see how. With i = 1, j = 1, the second term in the brackets is 0/0.
bugatti79 said:
I understand the the numerator in the first 2 terms inside the big brakets are both 0 when i=j=1 but we still yield a value from the third term...
Any insight will be appreciated
Regards

PS:I notice there is some editing problem with the 3 terms inside the big brackets. There should be a minus and plus separating the terms.
 
  • #3
Could you give a link to where you found this question?
 
  • #4
Mark44 said:
I don't see how. With i = 1, j = 1, the second term in the brackets is 0/0.

Are you saying because one of the terms is indeterminate then the whole equation is invalid and thus cannot be usedt o calcualte ##B_{ij}## for i=j=1?

micromass said:
Could you give a link to where you found this question?

See attached jpeg of question. The answer involves converting the DE into a weak form using a weight function w and splitting the differentiation between the weight function and the dependent variable u.
Would the choice of the approximation functions affect the outcome?

regards
 

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  • #5


It seems that the author is using indexing to determine the coefficients for the expression provided. This is a common method in mathematics and science to simplify complex equations and make them easier to understand and manipulate. In this case, the author is using the indices i and j to represent different variables in the equation.

However, it is important to note that this expression is not valid for certain values of i and j, specifically when i=1 and j=1,2,...N. This means that the author's calculations for B_{ij} in these cases may not be accurate.

It is possible that the author has made a mistake in their calculations for B_{11} and B_{1j}, as the first two terms inside the big brackets should result in a 0 value when i=j=1. However, the third term may still yield a value and this could be the reason for the discrepancy.

It would be helpful to have more information about the context in which this expression is being used, as well as any assumptions or limitations that the author may have mentioned. It may also be worth double-checking the calculations to ensure their accuracy.

Overall, indexing can be a useful tool for simplifying and organizing equations, but it is important to be aware of its limitations and potential for error.
 

What is indexing in the context of determining coefficients?

Indexing is the process of organizing and categorizing data in a way that makes it easier and faster to retrieve information from a large dataset. In the context of determining coefficients, indexing allows for quick access to specific data points that are relevant to calculating the coefficients.

Why is indexing important for determining coefficients?

Indexing is important for determining coefficients because it allows for efficient and accurate calculations. Without indexing, it would be difficult and time-consuming to retrieve the necessary data points for calculating coefficients.

What factors should be considered when choosing an indexing method for determining coefficients?

There are several factors to consider when choosing an indexing method for determining coefficients, such as the size and complexity of the dataset, the type of data being indexed, and the desired speed and accuracy of calculations. Additionally, the available resources and expertise of the research team should also be taken into account.

What are some common indexing methods used for determining coefficients?

Some common indexing methods used for determining coefficients include B-tree indexing, hash indexing, and binary search indexing. Each method has its own advantages and disadvantages, and the choice of method will depend on the specific needs of the research project.

How can the results of indexing be interpreted to determine coefficients?

The results of indexing can be interpreted by analyzing the relationship between different data points and identifying patterns and trends. This information can then be used to calculate coefficients and make conclusions about the data set. Additionally, the accuracy and reliability of the results should also be evaluated to ensure the validity of the coefficients.

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