You have an error in your code. Namely in the backward substitution. I think it should look like for k in range(n-2, -1, -1): x[k] = augmented_matrix[k][n] for j in range(k+1, n): x[k] = x[k] - augmented_matrix[k][j] * x[j] x[k] = x[k] / augmented_matrix[k][k]
@@banozz4793 It is not referred python it's concerned with Gauss elimination antilogarithm. During back substitution x[k] calculate as the last column of the augmented matrix minus the sum of all previously found x multiplied by the appropriate coefficient from the augmented matrix, and afterwords divided by a[k][k]. It is better to show by formulas than describe by words.
Yes you are absolutely right, In my reference code that I created and in my "Gauss Elimination With Partial Pivoting" video I have exactly what you presented here, thank you. I will pin this as the top comment so that others will see your contribution.
i believe you should add a link to copy the code so it is easier to follow and replicate. I am very new to coding and while I understand things, it all moves too fast for me to do it on my own. :)
I think that your code is not inclusive . if the first number in the matrix (matrix[0][0]) is zero , while everything else is ok , your code would stop immediately and get out of the function due to the first condition in the while loop , even though, generally when we do it manually we would simply swap rows and continue with the process . therefore your code does not include the cases when mat[0][0] = 0 .
The answer's wrong, also mistakes in the code. That's sad you are misleading people. You must double/triple check before publishing. A lot of useless variables, => memory loss. For example variable j = I - 1 Is not used at all.
You have an error in your code. Namely in the backward substitution. I think it should look like
for k in range(n-2, -1, -1):
x[k] = augmented_matrix[k][n]
for j in range(k+1, n):
x[k] = x[k] - augmented_matrix[k][j] * x[j]
x[k] = x[k] / augmented_matrix[k][k]
can you explain why? im a bit new to python
@@banozz4793 It is not referred python it's concerned with Gauss elimination antilogarithm. During back substitution x[k] calculate as the last column of the augmented matrix minus the sum of all previously found x multiplied by the appropriate coefficient from the augmented matrix, and afterwords divided by a[k][k]. It is better to show by formulas than describe by words.
Yes you are absolutely right, In my reference code that I created and in my "Gauss Elimination With Partial Pivoting" video I have exactly what you presented here, thank you. I will pin this as the top comment so that others will see your contribution.
Learn how to add partial pivoting to Gauss Elimination here: th-cam.com/video/DiZ0zSzZj1g/w-d-xo.html
Sir, thanks for your video. By the way, do you want to use C++ for Gaussian Elimination in another video? Thanks
Your backwards substitution bit is wrong. Please check again
i believe you should add a link to copy the code so it is easier to follow and replicate. I am very new to coding and while I understand things, it all moves too fast for me to do it on my own. :)
Dedicate a otra cosa Tal vez cocinar o modas o berlleza
It's sad there's such a big mistake in your code in the backward substitution.
I think that your code is not inclusive . if the first number in the matrix (matrix[0][0]) is zero , while everything else is ok , your code would stop immediately and get out of the function due to the first condition in the while loop , even though, generally when we do it manually we would simply swap rows and continue with the process . therefore your code does not include the cases when mat[0][0] = 0 .
Hi, how do I solve this issue?
I forgot about this code a long time ago I can’t help you at this point sorry
:)
The answer's wrong, also mistakes in the code. That's sad you are misleading people. You must double/triple check before publishing.
A lot of useless variables, => memory loss. For example variable j = I - 1 Is not used at all.