[Solved] Identify the error in this code snippet: import numpy as np from scipy.linalg import cholesky A = np.array([[4, 1], [1, 3]]) L = cholesky(A, lower=False) print(L) | SciPy

SciPy - Linear Algebra (scipy.linalg)

Identify the error in this code snippet:

import numpy as np
from scipy.linalg import cholesky
A = np.array([[4, 1], [1, 3]])
L = cholesky(A, lower=False)
print(L)

ANo error; code runs correctly

BUsing lower=False returns upper triangular matrix, not lower

CMatrix A is not positive definite

DMissing import for numpy

Step-by-Step Solution

Solution:

Step 1: Check matrix properties
Matrix A is symmetric and positive definite, so decomposition is valid.
Step 2: Understand lower parameter
Setting lower=False returns the upper triangular matrix U such that A = U.T * U, which is valid usage.
Final Answer:
No error; code runs correctly -> Option A
Quick Check:
lower=False returns upper triangular matrix [OK]

Quick Trick: lower=False returns upper triangular matrix [OK]

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