CODEWITHSUNDEEP
pythonnumpylinear-algebra
Occhima
Occhima

Reputation: 253

Multiplication of a matrix by a transpose vector

I'm implementing the inverse power method to find the maximum eigenvalue of a matrix.Given a matrix $A$ ( n by n matrix ) and a vector $x$ a np.array with shape: (len(A),): One of the steps of the implementation involves computing this value:

$q = x^TAx$

The thing is, i don't know if i'm implementing this the rigt way:

q = x.transpose() @ A @ x

Is there a better way to compute this?

Upvotes: 0

Views: 519

Answers (1)

Amir
Amir

Reputation: 318

x = x.reshape(-1, 1) # or x = x.reshape(1, -1) if x is a row vector. 
A_times_x = np.matmul(A, x)
q = np.matmul(x.T, A)

instead of np.matmul, you should be able to use np.dot as well, but the former is more readable in my opinion.

Upvotes: 1

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