a. If Ax = x for some scalar A, then x is an eigenvector of A. b. If v1 and v2 are linearly independent eigenvectors,

a. If Ax = λx for some scalar A, then x is an eigenvector of A.
b. If v1 and v2 are linearly independent eigenvectors, then they correspond to distinct eigenvalues.
c. A steady-state vector for a stochastic matrix is actually an eigenvector.
d. The eigenvalues of a matrix are on its main diagonal.
e. An eigenspace of A is a null space of a certain matrix.