Raising the bar Questions for repetition: applications of diagonalization

Oct 18

Questions for repetition

Questions for repetition: diagonal representation

Let $\lambda_i$ be the eigenvalues of a symmetric matrix $A.$ Prove that a) it is positive if and only if its eigenvalues are positive and b) it is non-negative if and only if its eigenvalues are non-negative. Reproduce my proof, which gives only sufficiency ( $\min_i\lambda_i>0$ implies positivity of $A$ and $\min_i\lambda_i\geq 0$ implies non-negativity of $A$ ). For the necessity part, plug eigenvectors of $A$ in $(Ax)\cdot x.$
Use the Cauchy-Hadamard theorem to relate the radius of convergence of the power series used to define a function of a matrix $f(A)$ to the radius of convergence of the function $f(t)$ of a numerical argument.
What is the matrix solution of the initial value problem $x^\prime(t)=Ax(t),$ $x(t_0)=x_0?$
How does the knowledge of the diagonal representation of $A$ simplify finding $f(A)?$
Describe how the knowledge of the diagonal representation of $A$ allows one to split the system $x^\prime(t)=Ax(t)$ into a collection of one-dimensional equations. How does this lead to the solution of the initial value problem in Exercise 3?
Define a square root of a non-negative symmetric matrix and relate it to the definition of the same using the power series. What are the properties of the square root?
Show that the variance matrix $\Omega$ of an arbitrary random vector $e$ (with real random components) is symmetric and non-negative.
When the matrix $\Omega$ is positive, what are the properties of $\Omega^{-1/2}?$
Find the variance of $\Omega ^{-1/2}e.$
How does the previous result lead to the Aitken estimator?
Define the absolute value of $A$ and show that this definition is correct.
If $A$ is diagonalized, what is the expression of its determinant in terms of its eigenvalues?
(This is strictly about ideas) Describe the elements of the polar form $c=\rho e^{i\theta}$ of a complex number. How do the definitions of $\rho$ and $e^{i\theta }$ help you define their analogs for matrices?
Derive the polar form for a square matrix.
More on similarity between complex numbers and matrices. For a square matrix with possibly complex entries, define the real part $\text{Re}(A)=(A+A^\prime)/2$ and the imaginary part $\text{Im}(A)=(A-A^\prime)/(2i).$ Here $A^\prime$ is the adjoint of $A.$ Show that both $\text{Re}(A)$ and $\text{Im}(A)$ are symmetric and that $A=\text{Re}(A)+i\text{Im}(A).$
Using population characteristics, describe the idea of Principal Component Analysis.
Show how this idea is realized in the sampling context.
This is a research problem to support Exercise 17. How do you change places of rows in a matrix? We want to find an orthogonal matrix $P$ such that premultiplication of $W$ by $P$ yields a matrix $W_1$ where $W_1$ has the same elements as $W$ except that some rows have changed their places. a) Consider a matrix $W$ of size $2\times 2$ and let $W_1$ be the transformed matrix (with the first row of $W$ as the second row of $W_1$ and vice versa). Find $P$ from the equation $PW=W_1.$ b) Do the same for a matrix $W$ of size $3\times 3.$ c) Generalize to the case of an $n\times n$ matrix $W,$ first considering matrices $P$ that change places of only two rows. Let's call such a matrix an elementary matrix. Note that it is orthogonal. d) The matrix that changes any number of rows is a product of elementary ones. It is orthogonal as a product of orthogonal matrices.