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New topic: **eigenproblems**. Reviewed the usual formulation of eigenproblems and the characteristic polynomial, mentioned extensions to generalized eigenproblems and SVDs.

Pointed out that an "LU-like" algorithm for eigenproblems, which computes the exact eigenvalues/eigenvectors (in exact arithmetic, neglecting roundoff) in a finite number of steps involving addition, subtraction, multiplication, division, and roots, is impossible. The reason is that no such algorithm exists (or can *ever* exist) to find roots of polynomials with degree greater than 4, thanks to a theorem by Abel, Galois and others in the 19th century. Discussed the connection to other classic problems of antiquity (squaring the circle, trisecting an angle, doubling the cube), which were also proved impossible in the 19th century.

As a result, all eigenproblem methods must be *iterative*: they must consist of improving an initial guess, in successive steps, so that it converges towards the exact result to *any desired accuracy*, but never actually reaches the exact answer in general. A simple example of such a method is Newton's method, which can be applied to iteratively approximate a root of any nonlinear function to any desired accuracy, given a sufficiently good initial guess.

However, finding roots of the characteristic polynomial is generally a terrible way to find eigenvalues. Actually computing the characteristic polynomial coefficients and then finding the roots somehow (Newton's method?) is a disaster, incredibly ill-conditioned: gave the example of Wilkinson's polynomial. If we can compute the characteristic polynomial values implicitly somehow, directly from the determinant, then it is not too bad (if you are looking only for eigenvalues in some known interval, for example), but we haven't learned an efficient way to do that yet. The way LAPACK and Matlab actually computes eigenvalues, the QR method and its descendants, wasn't discovered until 1960.

Discussed diagonalization, defective matrices, and the generalization ot the Schur factorization. Proved (by induction) that every (square) matrix has a Schur factorization, and that for Hermitian matrices the Schur form is real and diagonal.