Jordan Canonical Form
One representative per similarity class, assembled from blocks carrying an eigenvalue down the diagonal and ones just below it.
24 exercises
Exercise 1
Do the check for Example 2.4.
Exercise 2
Each matrix is in Jordan form. State its characteristic polynomial and its minimal polynomial.
-
(a) (b) (c) (d) (e) (f) (g) (h) (i)
Exercise 3
Find the Jordan form from the given data.
-
The matrix is with the single eigenvalue . The nullities of the powers are: has nullity two, has nullity three, has nullity four, and has nullity five.
-
The matrix is with two eigenvalues. For the eigenvalue the nullities are: has nullity two, and has nullity four. For the eigenvalue the nullities are: has nullity one.
Exercise 4
Find the change of basis matrices for each example.
-
(a) Example 2.12 (b) Example 2.13 (c) Example 2.14
Exercise 6
Find all possible Jordan forms of a transformation with characteristic polynomial .
Exercise 7
Find all possible Jordan forms of a transformation with characteristic polynomial .
Exercise 8
Find all possible Jordan forms of a transformation with characteristic polynomial and minimal polynomial .
Exercise 9
Find all possible Jordan forms of a transformation with characteristic polynomial and minimal polynomial .
Exercise 10
Diagonalize these.
-
(a) (b)
Exercise 11
Find the Jordan matrix representing the differentiation operator on .
Exercise 12
Decide if these two are similar.
Exercise 13
Find the Jordan form of this matrix.
Also give a Jordan basis.
Exercise 14
How many similarity classes are there for matrices whose only eigenvalues are and ?
Exercise 15
Prove that a matrix is diagonalizable if and only if its minimal polynomial has only linear factors.
Exercise 16
Give an example of a linear transformation on a vector space that has no non-trivial invariant subspaces.
Exercise 17
Show that a subspace is invariant if and only if it is invariant.
Exercise 18
Prove or disprove: two matrices are similar if and only if they have the same characteristic and minimal polynomials.
Exercise 19
The trace of a square matrix is the sum of its diagonal entries.
-
Find the formula for the characteristic polynomial of a matrix.
-
Show that trace is invariant under similarity, and so we can sensibly speak of the ‘trace of a map’. (Hint: see the prior item.)
-
Is trace invariant under matrix equivalence?
-
Show that the trace of a map is the sum of its eigenvalues (counting multiplicities).
- …
Exercise 20
To use Definition 2.10 to check whether a subspace is invariant, we seemingly have to check all of the infinitely many vectors in a (nontrivial) subspace to see if they satisfy the condition. Prove that a subspace is invariant if and only if its subbasis has the property that for all of its elements, is in the subspace.
Exercise 21
Is invariance preserved under intersection? Under union? Complementation? Sums of subspaces?
Exercise 22
Give a way to order the Jordan blocks if some of the eigenvalues are complex numbers. That is, suggest a reasonable ordering for the complex numbers.
Exercise 23
Let be the vector space over the reals of degree polynomials. Show that if then is an invariant subspace of under the differentiation operator. In , does any of , …, have an invariant complement?
Exercise 24
In , the vector space (over the reals) of degree polynomials,
and
are the even and the odd polynomials; is even while is odd. Show that they are subspaces. Are they complementary? Are they invariant under the differentiation transformation?
Exercise 25
A matrix is the square root of another if . Show that any nonsingular matrix has a square root.
Adapted from Linear Algebra © 2016 Jim Hefferon. Licensed under CC BY-SA 3.0 US. Source: Linear Algebra. Imported at revision df2262e.