PYTHON CODE MODIFICATION

The below code is used for solving a system of linear equations using Gaus-Jordan Elimination:

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# Define the matrix A and vector b

A = [[1, 0, 2], [2, -1, 3], [4, 1, 8]]

b = [[1], [-1], [2]]

# Combine A and b into augmented matrix C

C = [row_a + row_b for row_a, row_b in zip(A, b)]

# Get the number of rows and columns of the matrix

n = len(C)

# Set E to 1 (initially assume a unique solution exists)

E = 1

# Iterate through columns of matrix

for j in range(n):

# Find the row with the largest magnitude value in column j, only looking at rows j and below

pivot_row = max(range(j, n), key=lambda i: abs(C[i][j]))

p = C[pivot_row][j]

# Check if the pivot is zero

if p == 0:

E = 0

break

# If the pivot is not in row j, swap rows j and the pivot row

if pivot_row != j:

C[j], C[pivot_row] = C[pivot_row], C[j]

# Divide row j by pivot value

C[j] = [C[j][i] / p for i in range(n)]

# Subtract the pivot row from all other rows to eliminate the value at column j

for i in range(n):

if i != j:

factor = C[i][j]

C[i] = [C[i][k] - factor * C[j][k] for k in range(n)]

# Check if a unique solution was found

if E == 1:

x = [C[i][n-1] / C[i][i] for i in range(n)]

print("The solution is:", x)

else:

print("No unique solution exists.")

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MODIFICATION:

Create a new Python file and modify the code to compute the matrix inverse.

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THANK YOU!!!!! If your solution helps I will upvote!!!!!!!

(PLEASE DO NOT COPY AND PASTE FROM ANOTHER SOLUTION)