Ch04_1 4.8 Rank Definition Let A be an m n matrix. The rows of A may be viewed as row vectors r 1,...

Ch04_1

4.8 Rank

DefinitionLet A be an m n matrix. The rows of A may be viewed as row vectors r1, …, rm, and the columns as column vectors c1, …, cn. Each row vector will have n components, and each column vector will have m components, The row vectors will span a subspace of Rn called the row space of A, and the column vectors will span a subspace of Rm called the column space of A.

Rank enables one to relate matrices to vectors, and vice versa.

Ch04_2

Example 1

Consider the matrix

(1) The row vectors of A are

014561432121

(2) The column vectors of A are

Ch04_3

Theorem 4.16The row space and the column space of a matrix A have the same dimension.

DefinitionThe dimension of the row space and the column space of a matrix A is called the rank of A. The rank of A is denoted rank(A).

Ch04_4

Example 2Determine the rank of the matrix

852210321

A

Solution

Ch04_5

Theorem 4.17The nonzero row vectors of a matrix A that is in reduced echelon form are a basis for the row space of A. The rank of A is the number of nonzero row vectors.

Ch04_6

Example 3

Find the rank of the matrix

0000100001000021

A

Ch04_7

Theorem 4.18Let A and B be row equivalent matrices. Then A and B have the same the row space. rank(A) = rank(B).

Theorem 4.19

Let E be a reduced echelon form of a matrix A. The nonzero row vectors of E form a basis for the row space of A. The rank of A is the number of nonzero row vectors in E.

Ch04_8

Example 4Find a basis for the row space of the following matrix A, and determine its rank.

511452321

A

Ch04_9

4.9 Orthonormal Vectors and Projections

DefinitionA set of vectors in a vector space V is said to be an orthogonal set if every pair of vectors in the set is orthogonal. The set is said to be an orthonormal set if it is orthogonal and each vector is a unit vector.

Ch04_10

Example 1

5

3 ,

5

4 0, ,

5

4 ,

5

3 0,),0 ,0 ,1(

Solution

Show that the set is an orthonormal set.

Ch04_11

Theorem 4.20An orthogonal set of nonzero vectors in a vector space is linearly independent.

Proof

Ch04_12

DefinitionA basis that is an orthogonal set is said to be an orthogonal basis. A basis that is an orthonormal set is said to be an orthonormal basis.Standard Bases

• R2:• R3:• Rn:

Theorem 4.21Let {u1, …, un} be an orthonormal basis for a vector space V. Let v be a vector in V. v can be written as a linearly combination of these basis vectors as follows:

nn uuvuuvuuvv )()()( 2211

Ch04_13

Example 2The following vectors u1, u2, and u3 form an orthonormal basis for R3. Express the vector v = (7, 5, 10) as a linear combination of these vectors.

5

3 ,

5

4 0, ,

5

4 ,

5

3 0, 0), 0, 1,( 321 uuu

Solution

Ch04_14

Projection of One vector onto Another Vector

Figure 4.17

Let v and u be vectors in Rn with angle (0 ) between them.

Ch04_15

DefinitionThe projection of a vector v onto a nonzero vector u in Rn is denoted projuv and is defined by

uuuuv

vu proj

Figure 4.18O

Ch04_16

Example 3Determine the projection of the vector v = (6, 7) onto the vector u = (1, 4).

Solution

Ch04_17

Theorem 4.22The Gram-Schmidt Orthogonalization ProcessLet {v1, …, vn} be a basis for a vector space V. The set of vectors {u1, …, un} defined as follows is orthogonal. To obtain an orthonormal basis for V, normalize each of the vectors u1, …, un .

nnnn nvvvu

vvvu

vvuvu

uu

uu

u

11

21

1

projproj

projproj

proj

3333

222

11

Figure 4.19

Ch04_18

Example 4The set {(1, 2, 0, 3), (4, 0, 5, 8), (8, 1, 5, 6)} is linearly independent in R4. The vectors form a basis for a three-dimensional subspace V of R4. Construct an orthonormal basis for V.

Solution