- Give an example of a 2 2 matrix that has no real eigenvectors. Justify your solution with intuition (without solving completely for the eigenvectors and eigenvalues).
- Consider an np matrix A. Show that the number of linear independent rows is the same as the number of linearly independent columns.
Hint: Write A = CR where C is a matrix of the linearly independent columns of A. Why can we write A like this? Then consider the CR product in the row interpretation of matrix multiplication.
- Let A be an mn matrix (assume m > n). The full singular value factorization A = UV T contains more information than necessary to reconstruct A.
- What are the smallest matrices U, and VT such that UVT = A?
- Let . That is, think about U from the full singular value factorization as a block matrix consisting of the matrix U found in part (a) and the remaining (unneeded) columns U.
Find expressions for UTU and UUT.
- Use the reduced singular value factorization obtained in part (a) to find an expression for the matrix H = A(ATA)1AT. How many matrices must be inverted (diagonal and orthogonal matrices dont count)?
- Let x and y be vectors of m The least squares solution for a best-fit line for a plot of y versus x is
= (XTX)1XTy
where
- Suppose you know the full singular value factorization X = UV T. Find an expression for in terms of U, , and V . Hint: Only square matrices can be invertible.
- Repeat part (a) using the reduced singular value factorization X = UVT.
- Let X be an m n matrix (m > n) whose columns have sample mean zero, and let X = UVT be a reduced singular value factorization of X. The squared Mahalanobis distance to the point xiT (the ith row of X) is
d2i = xiTS1xi
where = cov(X). Explain how to compute d2i without inverting a matrix.
- (a) Suppose A = LU where L is lower triangular and U is upper triangular. Explain how you would solve the problem Ax = b using L, U, and the concepts of forward and backward substitution.
(b) Compute the LU factorization of
- 2 1
3 2
- 1 1
by hand using elimination matrices.
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