Sampling Strategies for Mining in Data-Scarce Domains
Computing in Science and Engineering
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Treating matrices as points in n2-dimensional space, we apply geometry to study and explain algorithms for the numerical determination of the Jordan structure of a matrix. Traditional notions such as sensitivity of subspaces are replaced with angles between tangent spaces of manifolds in n2-dimensional space. We show that the subspace sensitivity is associated with a small angle between complementary subspaces of a tangent space on a manifold in n2-dimensional space. We further show that staircase algorithm failure is related to a small angle between what we call staircase invariant space and this tangent space. The matrix notions in n2-dimensional space are generalized to pencils in 2mn-dimensional space. We apply our theory to special examples studied by Boley, Demmel, and Kågström.