# Summary for a matlab code

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Kareem on 13 Nov 2023
Commented: Walter Roberson on 13 Nov 2023
Hello, and thank your for taking your time to read this message, i just want to understand how this code works line by line.
function update = naive_gauss_jordan(J, b)
% Solve Ax = b using Naïve Gauss-Jordan elimination
[row, ~] = size(J);
augmentedMatrix = [J, b];
for i = 1:row
% Pivot
pivot = augmentedMatrix(i, i);
augmentedMatrix(i, :) = augmentedMatrix(i, :) / pivot;
% Elimination
for j = 1:row
if j ~= i
factor = augmentedMatrix(j, i);
augmentedMatrix(j, :) = augmentedMatrix(j, :) - factor * augmentedMatrix(i, :);
end
end
end
% Extract the solution
update = augmentedMatrix(:, end);
end
Walter Roberson on 13 Nov 2023
First off, do you agree that operations such division cannot be understood without a thorough understanding of arithmetic and mathematics?
Second of all, do you agree that it is not possible to understand how a line of computer code works without also understanding how computers process programs?
Third of all, do you agree that it is not possible to understand how computers process programs without an understanding of computer hardware, including how electricity works?
Fourth of all, since some of the ways that electricity is used in computer hardware depends upon quantum effects, do you agree that it is not possible to understand how computers work without studying quantum physics?
Fifth of all, do you agree that it is not possible to understand quanum physics without a good grounding in calculus and group theory?
In short, it seems to me that you have asked us to explain in detail a lot of mathematics and physics. Several advanced textbooks worth.

Cris LaPierre on 13 Nov 2023
Here's a summary created by ChatGPT. I have not checked it for correctness.
The provided MATLAB code implements the Naïve Gauss-Jordan elimination method to solve a system of linear equations represented by the matrix equation Ax = b. Here is a line-by-line description of what the MATLAB code is doing:
1. The line `function update = naive_gauss_jordan(J, b)` defines a function called `naive_gauss_jordan` that takes two input arguments: matrix `J` and vector `b`. It also declares that the output of the function will be assigned to the variable `update`.
2. The line `[row, ~] = size(J);` determines the number of rows in matrix `J` and assigns it to the variable `row`. The `~` is used to discard the second output of the `size()` function, which represents the number of columns.
3. The line `augmentedMatrix = [J, b];` creates a new matrix called `augmentedMatrix` by concatenating matrix `J` and vector `b` horizontally. This matrix represents the augmented matrix [J | b].
4. The line `for i = 1:row` starts a loop that iterates from 1 to the number of rows in matrix `J`.
5. The line `pivot = augmentedMatrix(i, i);` retrieves the pivot element from the `i`-th row and `i`-th column of `augmentedMatrix` and assigns it to the variable `pivot`. The pivot element is the main diagonal element of the current row being processed.
6. The line `augmentedMatrix(i, :) = augmentedMatrix(i, :) / pivot;` divides the entire `i`-th row of `augmentedMatrix` by the pivot element, normalizing the row.
7. The line `for j = 1:row` starts an inner loop that iterates from 1 to the number of rows in matrix `J`.
8. The line `if j ~= i` checks if the current row `j` is not the same as the current row being processed `i`.
9. The line `factor = augmentedMatrix(j, i);` retrieves the element at the `j`-th row and `i`-th column of `augmentedMatrix` and assigns it to the variable `factor`. This represents the factor by which the current row will be subtracted.
10. The line `augmentedMatrix(j, :) = augmentedMatrix(j, :) - factor * augmentedMatrix(i, :);` subtracts the product of the factor and the `i`-th row from the `j`-th row of `augmentedMatrix`. This eliminates the variable corresponding to the `i`-th column in the `j`-th row.
11. The loop continues until all rows have been processed.
12. The line `update = augmentedMatrix(:, end);` extracts the last column of `augmentedMatrix`, which represents the solution vector, and assigns it to the variable `update`.
13. Finally, the line `end` marks the end of the function `naive_gauss_jordan`.
In summary, this code performs the Naïve Gauss-Jordan elimination method to solve a system of linear equations and returns the solution vector.
Cris LaPierre on 13 Nov 2023
If you don't understand the code already, that may not be helpful. Here's a slightly more generalized description, also created by ChatGPT.
The provided MATLAB code implements the Naïve Gauss-Jordan elimination method to solve a system of linear equations represented by the matrix equation Ax = b. Here is a summary of the code:
1. The function `naive_gauss_jordan` takes two inputs: matrix `J` representing the coefficients of the linear equations, and vector `b` representing the constants on the right-hand side of the equations.
2. The size of matrix `J` is determined, and a new matrix called `augmentedMatrix` is created by appending vector `b` to the right of matrix `J`.
3. A loop is initiated to perform Gauss-Jordan elimination row by row.
4. Within the loop, the current row's pivot element is obtained from `augmentedMatrix(i, i)`.
5. The current row is then divided by the pivot element to normalize it.
6. Another loop is used to eliminate the variables in the remaining rows.
7. For each row that is not the current row (`j ~= i`), a factor is calculated by dividing the corresponding element in `augmentedMatrix(j, i)` by the pivot element.
8. The current row is subtracted by the product of the factor and the current row, effectively eliminating the variable in that row.
9. After completing the elimination process for all rows, the solution is extracted from the last column of `augmentedMatrix` and assigned to the variable `update`.
10. Finally, the function returns the solution vector `update`.
In summary, this code performs the Naïve Gauss-Jordan elimination method to solve a system of linear equations and returns the solution vector.

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