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In underdetermined linear systems (more variables than equations), gradient descent started at zero converges to the minimum Euclidean norm solution without any explicit regularizer.
Matrix norms measure the size of a matrix in different but related ways, with Frobenius treating entries like a big vector, spectral measuring the strongest stretch, and nuclear summing all singular values.