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It can be done. Back in the 1990s certain groups (think radar cross section computation) were working with 1,000,000 x 1,000,000 dense systems. Consistent with other replies, this was done iteratively and out of core.


Let us proceed systematically: numerical precision of data (you said from medical imaging) number of operations required for standard methods (as from libraries) possible out-of-core computation (i.e. not the whole matrix at all times in memory). In all cases, I am afraid, you would have to be prepared to suffer. Incidentally, out-of-core methods are very ...


Take a look at the literature that does similar things for facial recognition -- search for the term "eigenface", for example. The point to make in this context is that the information you are looking for does not actually require you to consider high-resolution images. You may have $10,000\times 10,000$ pixels, for which any non-trivial ...


As noted above by Thijs Steel a randomized svd is a solution but the number 78800000 is out of our computers computation ability.So you can proceed to the rsvd algorithm by : import numpy as np n = 788 mu = 0 sigma = 1 A = np.random.normal(mu, sigma, (n,n)) Omega = np.random.normal(mu, sigma, (n,n)) def rsvd(A, Omega): Y = A @ Omega Q, _ = np....

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