- Compute the product Matrix([[1,2],[2,1])* Vector([1,1])
by using matrix multiplication. Next
check to verify that it satisfies the
definition of eigenvector: Ax =lambda x, where
A=Matrix([[1,2],[2,1]) and x=Vector([1,1]).
What is lambda, the eigenvalue corresponding to x=Vector([1,1])?
a) 1
b) 2
c) 3
d) -1
e) none of the above
- Compute the product Matrix([[1,2],[2,1])4 * Vector([1,1])
by repeatedly
using the definition of eigenvector rather than matrix/vector multiplication.
Hint: A4x = A(A(A(Ax)))) via
associativity, so keep substituting in Ax =lambda x and pulling out the
scalar lambda. Repeate this process 4 times.
a) Vector([1,1])
b) Vector([3,3])
c) Vector([12,12])
d) Vector([81,81])
e) None of the above
- An eigenvector allows us to turn:
a) Matrix multiplication into matrix addition
b) Matrix addition into matrix multiplication
c) Matrix multiplication into scalar multiplication
d) Matrix addition into scalar multiplication
e) none of the above
- For any integer n, what will this product be?
Matrix([[1,2],[2,1])n * Vector([1,1]).
a) Vector([3n,3n])
b) 3n Vector([1,1])
c) n3 Vector([1,1])
d) 3n Vector([n,n])
e) Vector([3,3])n
- Write Vector([1,5]) as a linear combination of the eigenvectors
Vector([1,1]) and Vector([1,-1])
a) 2*Vector([1,0]) + 3*Vector([0,1])
b) 2*Vector([0,1]) + 3*Vector([1,3])
c) 2*Vector([-1,1]) + 3*Vector([1,1])
d) 2*Vector([1,1]) + 3*Vector([-1,1])
e) none of the above
- For any integer n, what will this product be?
Matrix ([[1,2],[2,1]) n Vector([1,5])
a) -1 * 3n Vector([1,1]) + 3* (-2)n Vector([1,-1])
b) 3 * (-1)n Vector([1,1]) + (-2)* (3)n Vector([1,-1])
c) 3 * 3n Vector([1,1]) + (-2)* (-1)n Vector([1,-1])
d) 3 * 3n Vector([1,1]) + (-1)* (-2)n Vector([1,-1])
e) None of the above
-
How many linearly independent eigenvectors does
Matrix([[1,2],[2,1]]) have?
a) 0
b) 1
c) 2
d) infinite
e) none of the above
- How many eigenvectors does
Matrix([[1,2],[2,1]]) have?
a) 0
b) 1
c) 2
d) infinite
e) none of the above