There is a really cool technique for iteratively computing t…
There is a really cool technique for iteratively computing the eigenvector associated with the dominant eigenvalue, which can be used to compute Google’s PageRank vectors. For our purposes here, all we care about is that we can find a useful vector using an iterative approach as we discussed in class. The recurrence relation to compute a new value from the old is: for some square matrix and nonzero initial guess vector, . Give an appropriate assignment statement to complete the loop in the following code so that vector variable x has the result value after the loop. Recall np.dot(q,r) does vector or matrix multiplication according to the shapes of q and r. Some partial credit will be given. x = np.array([1,1,1]) # this is x_0for i in range(10): __________________________________
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