Math 308 Practice Final Exam
Problem 1: Solving a linear equation
Write a basis for the null space of A. Basis = ___________________
(b) What is the dimension of the range of A? Dimension = _____
(c) Is y in the span of the row vectors of A? Yes? ___ No? ___
Problem 2: Conclusions from echelon form.
In each case, we start with a matrix A and vector and tell
what one will get by reducing
the augmented matrix of the system Ax = y to echelon form. Answer the questions in
each case using this information. It is possible some equations will have no solution.
|A||y||Echelon form of augmented
matrix of Ax = y.
|(a) Write the general solution for Ax
= y in (vector) parametric form
(b) What is the dimension of the null space of A? Dimension = ____
(c) Write down a basis for the null space of A.
|B||z||Echelon form of augmented matrix of Bx = z.|
|(a) Write the general solution for Bx
= z in (vector) parametric form
(b) What is the dimension of the null space of B? Dimension = ______
(c) Write down a basis for the null space of B. Basis =
(d) Is z in the range of B? Yes? No?
(e) What is the dimension of the range of B? Dimension = ______
(f) Write down a basis of the range of B. Basis =
(g) Are the columns of B independent? Yes? No?
|C||Reduced row echelon form of C.|
|(a) Is C invertible?
Yes? ___ No? ___
(b) Are the columns of C independent? Yes? ___ No? ___
(c) Write down a basis for the null space of C.
Problem 3: Compute matrix products and inverses
Compute the stated matrix products (if defined) for these matrices.
Compute each of the following matrix products or other matrices (if defined):
Problem 4: Find the eigenvalues and eigenvectors
Find the eigenvalues and eigenvectors of matrix
If possible, diagonalize F, i.e., write F = UDV, where D is diagonal.
U =_____________ D = ________ V =_______________
Problem 5: Given the eigenvalues find the eigenvectors
Given that 1 and -3 are the eigenvalues of the matrix
eigenvectors of this matrix. Hint: First find whether
there is a third eigenvalue. If you
compute the determinant of C, knowing 2 eigenvalues, you can find the third root of the
characteristic polynomial without computing det(C - λI).
If possible, diagonalize C, i.e., write C = MDN,
where D is diagonal. You DO NOT
need to compute the inverse of any matrix in this problem. If a matrix is the inverse of a
known matrix, just write it as the inverse.
M =________ D =______________ N =__________________
Problem 6: Compute orthogonal projections
Problem 7: Matrix of Linear Transformation
(a) If T is the linear transformation of R2 that rotates the plane by an angle
so that the
point (1, 0) is rotated to (3/5, 4/5). What is the matrix A of this transformation?
(b) Is the matrix A an orthogonal matrix? Yes? ___ No? ___
(c) Is the matrix 2A an orthogonal matrix? Yes? ___
Show why in both cases.
(d) Given the vector if x is a vector in R3, then let T(x) = (u ∙ x)u. Is T a linear
transformation? If so, what is its matrix (with respect to the standard basis).
Problem 8: Least squares solution
You have the following data for variables x , y, z
(a) Now suppose that you want to fit this data to a
function z = a + bx + cy, where, a, b, c
are some constants that you need to solve for. Write a system of equations for
variables a, b, c that can be solved if there is an exact fit of the function to the data.
(b) Find the lease-squares "solution" a, b, c to this system and check the
values of z = a +
bx + cy at the data points to see how close a fit it is.
Least squares solution : z = ___ + ___ x + ___ y