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⚡️ Speed up function matrix_decomposition_LU by 1,015% #58

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⚡️ Speed up function matrix_decomposition_LU by 1,015% #58

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12 changes: 5 additions & 7 deletions src/numpy_pandas/matrix_operations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,16 +60,14 @@ def matrix_decomposition_LU(A: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
L = np.zeros((n, n))
U = np.zeros((n, n))
for i in range(n):
# Compute the U[i, k] entries using vectorized dot product
Li = L[i, :i]
for k in range(i, n):
sum_val = 0
for j in range(i):
sum_val += L[i, j] * U[j, k]
U[i, k] = A[i, k] - sum_val
U[i, k] = A[i, k] - np.dot(Li, U[:i, k])
L[i, i] = 1
Ui = U[:i, i]
for k in range(i + 1, n):
sum_val = 0
for j in range(i):
sum_val += L[k, j] * U[j, i]
sum_val = np.dot(L[k, :i], Ui)
if U[i, i] == 0:
raise ValueError("Cannot perform LU decomposition")
L[k, i] = (A[k, i] - sum_val) / U[i, i]
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