Section 4.4 Rank of a Matrix

Definition:  A is a m × n matrix. The column space, Col A, of A is subspace spanned by columns of A. The row space, Row A, of A is the subspace of Rn spanned by rows of A.

Fact: If A is a reduced-echelon matrix, then the nonzero rows of basis of RowA. The pivot columns of A are a basis of ColA.

Definition: The rank of a matrix [latex]A[/latex], denoted by rank[latex]A[/latex], is the dimension of the column space of [latex]A[/latex].

Fact:

1. dim(Col[latex]A[/latex]) = dim(Row[latex]A[/latex]) = rank[latex]A[/latex].

2. rank[latex]A[/latex] = rank[latex]A^{T}[/latex]

The Rank Theorem: If a Matrix [latex]A[/latex] has n columns, then rank[latex]A[/latex] + dim Nul[latex]A[/latex] = n

Example 1: If the subspace of all solutions of [latex]A\overrightarrow{x}=0[/latex]
has a basis consisting of three vectors and if [latex]A[/latex] is a 5 x 8 matrix, what is the rank of [latex]A[/latex]?

Exercise 1: What is the rank of a 4 x 7 matrix whose null space is two-dimensional?

Example 2: Suppose a 4 x 6 matrix A has 4 pivot columns.

Is Col[latex]A=\mathbb{R}^{4}?[/latex]

Is Nul[latex]A=\mathbb{R}^{2}[/latex]?

Explain your answer.

Exercise 2: Suppose a 4 x 7 matrix A has 3 pivot columns.
Is Col[latex]A=\mathbb{R}^{3}?[/latex]

What is the dimension of Nul[latex]A[/latex]?

Explain your answer.

The Invertible Matrix Theorem:
Let [latex]A[/latex] be an [latex]n\times n[/latex] matrix.
Then the following statements are each equivalent to the statement that [latex]A[/latex] is an invertible matrix.

(a) The columns of [latex]A[/latex] form a
basis of [latex]\mathbb{R}^{n}[/latex]

(b) Col[latex]A=\mathbb{R}^{n}[/latex]

(c) dimCol[latex]A[/latex]=n

(d) rank[latex]A[/latex]=n

(e) Nul[latex]A\{0\}[/latex]

(f) dimNul[latex]A[/latex]=0

Theorem: The following are equivalent for an m x n matrix [latex]A[/latex]:

1. rank[latex]A[/latex]=n.

2. The rows of [latex]A[/latex] span [latex]\mathbb{R}^{n}[/latex].

3. The columns of [latex]A[/latex] are linearly independent in [latex]\mathbb{R}^{m}[/latex].

4. The n x n matrix [latex]A^{T}A[/latex] is invertible

5. [latex]CA=I_{n}[/latex] for some n x m matrix [latex]C[/latex].

6. If [latex]A\overrightarrow{x}=0[/latex],[latex]\overrightarrow{x}[/latex] in [latex]\mathbb{R}^{n}[/latex], then [latex]\overrightarrow{x}=0[/latex]

Theorem: The following are equivalent for an [latex]m\times n[/latex] matrix [latex]A[/latex]:

1. rank[latex]A=m[/latex]

2. The columns of [latex]A[/latex] span [latex]\mathbb{R}^{m}[/latex].

3. The rows of [latex]A[/latex] are linearly independent in [latex]\mathbb{R}^{n}[/latex]

4. The [latex]m\times m[/latex] matrix [latex]AA^{T}[/latex] is invertible.

5. [latex]AC=I_{m}[/latex] for some [latex]n\times m[/latex] matrix [latex]C[/latex].

6. [latex]A\overrightarrow{x}=\overrightarrow{b}[/latex] is consistent for every [latex]\overrightarrow{b}[/latex] in [latex]\mathbb{R}^{m}[/latex].

Example 3: If [latex]A[/latex] is an [latex]m\times n[/latex] matrix and rank[latex]A=m[/latex],
show that [latex]m\leq n[/latex].

Exercise 3: If [latex]A[/latex] is an [latex]m\times n[/latex] matrix and columns of [latex]A[/latex] are linearly independent, show Nul[latex]A=\{\overrightarrow{0}\}[/latex].

Group Work Example 1: True or False. Justify each answer:

a. Each line in [latex]\mathbb{R}^{n}[/latex] is a one-dimensional subspace of [latex]\mathbb{R}^{n}[/latex]

b. The dimension of Col[latex]A[/latex] is the number of pivot columns of [latex]A[/latex].

c. The dimensions of Col[latex]A[/latex] and Nul[latex]A[/latex] add up to the number of columns of [latex]A[/latex]

d. If a set of [latex]p[/latex] vectors spans a [latex]p[/latex]-dimensional subspace [latex]H[/latex] of [latex]\mathbb{R}^{n}[/latex], then these vectors form a basis for [latex]H[/latex].

e. The columns of an invertible [latex]n\times n[/latex] matrix form a basis for [latex]\mathbb{R}^{n}[/latex].

f. The dimension of Nul [latex]A[/latex] is the number of variables in the equation [latex]A\overrightarrow{x}=0[/latex]. The dimension of the column space of [latex]A[/latex] is rank[latex]A[/latex]

h. If [latex]H[/latex] is a [latex]p[/latex]-dimensional subspace of [latex]\mathbb{R}^{n}[/latex], then a linearly independent set of [latex]p[/latex] vectors in [latex]H[/latex] is a basis for [latex]H[/latex].

Group Work 2: Suppose [latex]F[/latex] is a [latex]5\times5[/latex] matrix whose column space is not equal to [latex]\mathbb{R}^{5}[/latex].
What can you say about Nul [latex]F[/latex] ?

Group Work 3: Construct a nonzero [latex]3\times4[/latex] matrix [latex]A[/latex] such that dimNul[latex]A=2[/latex] and dimCol[latex]A=2[/latex]

a. Can a [latex]3\times4[/latex] matrix have independent columns? Independent rows? Explain

b. If [latex]A[/latex] is [latex]4\times3[/latex] and rank[latex]A=2[/latex], can [latex]A[/latex] have independent columns? Independent rows? Explain

c. Can a non-square matrix have its rows independent and its columns independent? Explain

d. Can the null space of a [latex]3\times6[/latex] matrix have dimension [latex]2[/latex]? Explain

Group Work 5: Let [latex]A[/latex] be an [latex]n\times p[/latex] matrix whose column space is [latex]p[/latex]-dimensional. Explain why the columns of [latex]A[/latex] must be linearly independent

Group Work 6: Construct a [latex]4\times3[/latex] matrix with rank [latex]1[/latex]