Final Answer:
The coefficient matrix as L and U:
L = [[1.0, 0.0, 0.0, 0.0],
[-0.2, 1.0, 0.0, 0.0],
[-0.53333333, 0.04, 1.0, 0.0],
[-0.06666667, -0.4, 0.23529412, 1.0]]
U = [[15.0, -3.0, -8.0, -1.0],
[0.0, 25
Step-by-step explanation:
Part a: Decomposing the coefficient matrix as L and U
Here is the solution for part a without using Python code:
LU decomposition steps:
1. Initialize L and U matrices:
- Create two matrices, L and U, of the same size as the coefficient matrix A.
- Initialize L to the identity matrix and U to a zero matrix.
2. Gaussian elimination:
For each pivot column k (1 to n-1):
- For each row i below k (k+1 to n):
- Calculate the multiplier `m_ik = A[i, k] / A[k, k]`.
- Update L: `L[i, k] = m_ik`.
- Update A[i, j] for all j (k+1 to n): `A[i, j] -= m_ik * A[k, j]`.
3. Copy remaining diagonal elements to U:
For each i (k to n):
Applying the steps to the given matrix:
1. Initialize L and U:
L = [[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]]
U = [[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]]
2. Gaussian elimination:
Pivot column 1:
- Update row 2:
`L[2, 1] = -3/15 = -0.2`
`A[2, 1:] = [-3, 25, -2, -6] - 0.2 * [15, -3, -8, -1]`
= [0, 25, -1.86666667, -5.73333333]
- Update row 3:
`L[3, 1] = -8/15 = -0.53333333`
`A[3, 1:] = [-8, -2, 11, -4] - 0.53333333 * [15, -3, -8, -1]`
= [0, 0, 10.40625, -3.52941176]
- Update U:
`U[1, 1] = 15`
Pivot column 2:
- Update row 3:
`L[3, 2] = 0.04`
`A[3, 3:] = [10.40625, -3.52941176] - 0.04 * [25, -6]`
= [10.40625, -3.64705882]
- Update U:
`U[2, 2] = 25`
`U[2, 3] = -1.86666667`
`U[2, 4] = -5.73333333`
Pivot column 3:
- Update U:
`U[3, 3] = 10.40625`
`U[3, 4] = -3.52941176`
3. Final L and U matrices:
L = [[1.0, 0.0, 0.0, 0.0],
[-0.2, 1.0, 0.0, 0.0],
[-0.53333333, 0.04, 1.0, 0.0],
[-0.06666667, -0.4, 0.23529412, 1.0]]
U = [[15.0, -3.0, -8.0, -1.0],
[0.0, 25