This function inverts ill conditioned matrices using an iterative solution to the Tikhonov regularization problem. It takes three arguments: A, the matrix, l, lambda the contraint, and k, the number of iterations. In this iterative Tikhonov regularization model, also known as ridge regression, I introduce an iterative solution to the ill-posed l…
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ip.optimalk(A) #this will print out optimal k

ip.invert(A,be,k,l) #this will invert your A matrix, where be is noisy be, k is the no. of iterations, and lambda is your dampening effect (best set to 1)