[Solved] Identify the error in this code using cg solver:import numpy as np from scipy.sparse.linalg import cg A = np.array([[0, 1], [1, 0]]) b = np.array([1, 2]) x, info = cg(A, b) print(x) | SciPy

SciPy - Sparse Linear Algebra

Identify the error in this code using cg solver:

import numpy as np
from scipy.sparse.linalg import cg

A = np.array([[0, 1], [1, 0]])
b = np.array([1, 2])
x, info = cg(A, b)
print(x)

AMatrix A is not symmetric positive definite, so cg will fail.

BVector b has wrong shape.

Ccg function is not imported correctly.

DNo error; code runs fine.

Step-by-Step Solution

Solution:

Step 1: Check matrix properties
Matrix A = [[0,1],[1,0]] is symmetric but not positive definite (its eigenvalues are 1 and -1).
Step 2: Understand cg requirements
The conjugate gradient method requires A to be symmetric positive definite. Since A is not, cg will fail or not converge properly.
Final Answer:
Matrix A is not symmetric positive definite, so cg will fail. -> Option A
Quick Check:
cg needs symmetric positive definite matrix [OK]

Quick Trick: Check matrix eigenvalues before using cg [OK]

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Identify the error in this code using cg solver: