a) From the spectral decomposition of A, the eigenvalues of A are 2, -1,c. As det(A)=0, so the product of the eigenvalues of A is 0. Hence c=0 and the eigenvalues are 2,-1,0.
b) From spectral
decomposition,
A=.5[1 0 -3; 0 0 0; -3 0 1]. As v is eigenvector corresponding to
eigenvalue 0, v=[0 1 0]^t.
c) Let w=[1/√2 0 -1/√2]^t, u=[1/√2 0 1/√2]^t and D=diag (-1,0,2). As A=UDU^t,
U=[u v w]=[1/√2 0 1/√2; 0 1 0; 1/√2 0 -1/√2].
d) for x in R^3, let Y=U^t x in x^t Ax=a^2-b^2, we get Y^t DY=a^2-b^2, or 2(y3)^2-(y1)^2=a^2-b^2.
So y3=a/√2, y1=b. [ Where Y=(y1 y2 y3)^t].
Now Y=[(x1+x3)/√2 x2 (x1-x3)/√2]^t.
Hence x1=(a+b√2)/√2, x3=(b√2-a)/√2, take x2=0.
x=[(a+b√2)/√2 0 (b√2-a)/√2]^t.
Question 4. The spectral decomposition (or the orthogonal eigenvalue decomposi- tion) of a matrix A whose...
U is a 2 x 2 orthogonal matrix of determinant -1. Find 5 · [0, 1] · U if 5 · [1,0] · U = (-3,4]. 2. Let M = [[144, 18], [18, 171]]. Notice that 180 is an eigenvalue of M. Let U be an orthogonal matrix such that U-MU is diagonal, the first column of U has positive entries, and det(U) = 1. Find 145 · U.
2. Use the spectral decomposition (in reverse) to find the matrix A such that (1,-1,1) is an eigenvector with eigenvalue 2, and (2, 3, 1) and (4,-1,5) are eigenvectors with eigenvalue-3.
2. Use the spectral decomposition (in reverse) to find the matrix A such that (1,-1,1) is an eigenvector with eigenvalue 2, and (2, 3, 1) and (4,-1,5) are eigenvectors with eigenvalue-3.
2. The spectral decomposition theorem states that the eigenstates of any Hermitian matrix form an orthonormal basis for the linear space. Let us consider a real 3D space where a vector is denoted by a 3x1 column vector. Consider the symmetric matrix B-1 1 1 Show that the vectors 1,0, and1are eigenvectors of B, and find 0 their eigenvalues. Notice that these vectors are not orthogonal. (Of course they are not normalized but let's don't worry about it. You can...
#9. Which of the following is not necessarily a valid factorization of the given matrix M? (A) if M is any square matrix, then M = QR, where Q and R are both orthogonal matrices (B) if M has linearly independent columns, then M = QR where Q has orthonormal columns and R is an invertible upper triangular matrix (C) if M is a real symmetric matrix, then M = QDQT for some orthogonal matrix Q and diagonal matrix D...
(1 point) All vectors are in R". Check the true statements below: A. Not every orthogonal set in R™ is a linearly independent set. B. If a set S= {ui,...,Up} has the property that uiU;=0 whenever i+j, then S is an orthonormal set. C. If the columns of an m x n matrix A are orthonormal, then the linear mapping 1 → Ax preserves lengths. D. The orthogonal projection of y onto v is the same as the orthogonal projection...
Question 5 [3+(2+4) marks] (a) The matrix A has a repeated eigenvalue of 1 = 2. During the solution of the solution (A-21)X = 0, the augmented matrix below appears. Find a basis for the eigenspace for this eigenvalue. Ti 0 -2 07 lo o o lo To ooo (b) (i) Show that if T(x) is a linear transformation from R" to R", that T(0) is the zero vector. (i) Assume that T(u) = 0 only when u = 0....
[12] QUESTION 4 (a) Let A be an m × m symmetric matrix and P be an orthogonal matrix such the PAP-D,where D is a diagonal matrix with the characteristic roots of A on the diagonal. Show that PA P is also a diagonal matrix. (b) Let A be an m × n matrix of rank m such that A = BC where B and C each has rank m. Show that (BC) CB. 16 STA4801/101/0/2019 (c) For the matrix...
True False a) For nxn A, A and AT can have different eigenvalues. b) The vector v 0 cannot be an eigenvector of A. c) If λ's an eigenvalue of A, then λ2 is an eigenvalue of A2. True False d) If A is invertible, then A is diagonalizable. e) If nxn A is singular, then Null(A) is an eigenspace of A. f) For nxn A, the product of the eigenvalues is the trace of A. True False g) If...
Question 3: (a) (4 points) Recall that the trace of a square matrix is the sum of all its entries from the main diagonal. Show that the trace is linear, in the sense that, trace(aX + βΥ) trace(X) + β trace(Y). Let V be the space of all m × n matrices. A function <..) : V × V → R is defined as (A, B) trace(ABT), A, B E V. (a) (4 points) Using the properties of the trace,...
For the 3×2 matrix A:
a) Determine the eigenvalues of ATA, and confirm that
your eigenvalues are consistent with the trace and determinant of
ATA.
b) Find an eigenvector for each eigenvalue of
ATA.
c) Find an invertible matrix P and a diagonal matrix D such that
P-1(ATA)P = D.
d) Find the singular value decomposition of the matrix A; that
is, find matrices U, Σ, and V such that A = UΣVT.
e) What is the best rank 1...