Lecture 22: Section 4.7

Transcription

1 Lecture 22: Section 47 Shuanglin Shao December 2, 213

2 Row Space, Column Space, and Null Space Definition For an m n, a 11 a 12 a 1n a 21 a 22 a 2n A = a m1 a m2 a mn, the vectors r 1 = [ a 11 a 12 a 1n ] r 2 = [ a 21 a 22 a 2n ] r m = [ a m1 a m2 a mn ] in R n that are formed from the rows of A are called the row vectors of A,

3 and the vectors a 11 a 21 c 1 =, c 2 = a 12 a 22,, c n = a 1n a 2n a m1 a m2 a mn in R m formed from the columns of A are called the column vectors of A

4 Example Let The row vectors of A are A = [ ] r 1 = [2, 1, ], r 2 = [3, 1, 4] and the column vectors of A are [ ] [ 2 1 c 1 =, c 3 2 = 1 ] [, c 3 = 4 ]

5 Def Definition If A is an m n matrix, then the subspace of R n spanned by the row vectors of A is called the row space of A, and the subspace of R m spanned by the column vectors of A is called the column space of A The solution space of the homogeneous system of equations Ax =, which is a subspace of R n, is called the null space of A

6 Theorem A system of linear equations Ax = b is consistent if and only if b is in the column space of A Proof The linear system Ax = b is a 11 a 12 a 1n a 21 a 22 a 2n a m1 a m2 a mn x 1 x 2 x n = b 1 b 2 b n

7 This linear system can be written as a 11 a 12 a 21 x 1 + x a x n a m1 This proves the theorem a m2 a 1n a 2n a mn = b 1 b 2 b m

8 Example 2 Example 2 Let Ax = b be the linear system x x 2 = x 3 3 Solving the linear system by Gaussian elimination yields x 1 = 2, x 2 = 1, x 3 = 3 Thus from the theorem above, = 1 9 3

9 Theorem If x is any solution of a consistent linear system Ax = b, and if S = {v 1, v 2,, v k } is a basis for the null space of A, then every solution of Ax = b can be expressed in the form x = x + c 1 v 1 + c 2 v c k v k Conversely, for all choices of scalars c 1, c 2,, c k, the vector x in this formula is a solution of Ax = b

10 Bases for row spaces, column spaces, and null spaces Theorem Elementary row operations do not change the null space of a matrix Theorem Elementary row operations do not change the row space of a matrix Both proofs follow if we consider the three elementary row operations: exchange two rows, a nonzero constant multiplying a row, adding a multiple of a row to another row

11 We just learned from the elementary row operations do not change the row space of a matrix However the elementary row operations do change the column space of a matrix For instance, let A = [ ]

12 [ 1 The column space is spanned by 1 elementary row operations, we obtain [ 1 1 A = ] Applying the third [ ] 1 The column space is spanned by The spanning vectors are different, which generate different column spaces ]

13 Finding a basis for the null space of a matrix A = Solving the equation Ax =, we obtain x = r s t 2 1 These three vectors generate the null space

14 Basis for row spaces and column spaces Theorem If a matrix R is in row echelon form, then the row vectors with the leading 1 s (the nonzero row vectors ) form a basis for the row space of R, and the column vectors with the leading 1 s of the row vectors form a bassi for the column space of R Remark Only when a matrix is in the row echelon form, the columns with leading 1 s of the row vectors form a basis for the column space In general, it is not true for the original matrix since the elementary row operations will change the column space

15 Example Bases for row and column spaces The matrix A = The basis for the row space is r 1 = [ ], r 2 = [ 1 3 ], r 3 = [ 1 ]

16 The basis for the column space is c 1 = 1, c 2 = 2 1, c 3 = 1

17 Example: basis for a row space by row reduction Find a basis for the row space of a matrix A = By row reductions, we have R =

18 The basis for the row space is r 1 = [ ], r 2 = [ ], r 3 = [ 1 5 ]

19 We have talked about how to find a basis for the null space of a matrix by solving the linear system; how to find a basis for the row space by using the elementary row operations and select the rows with the leading 1 s In the remark above: only when a matrix is in the row echelon form, the columns with leading 1 s of the row vectors form a basis for the column space In general, it is not true for the original matrix since the elementary row operations will change the column space We will see how to find a basis for the column space for a matrix

20 Definition If B is obtained from A by applying elementary row operations to A, then A and B are said to be row equivalent Theorem (a) (b) If A and B are row equivalent matrices, then A given set of column vectors of A is linearly independent if and only if the corresponding column vectors are linearly independent A given set of column vectors of A forms a basis for the column space of A if and only if the corresponding column vectors of B form a basis for the column space of B

21 Example Find a basis by row reductions A = By row reductions, we have R = The 1st, 3rd, 5th columns form a basis for the matrix R By the theorem above, since A and R are row equivalent, the 1st, 3rd, 5th columns of the matrix A form a basis for A

22 Basis for a vector space using row operations Find a basis for the subspace of R 5 spanned by the vectors and v 1 = (1, 2,,, 3), v 2 = (2, 5, 3, 2, 6), v 3 = (, 5, 15, 1, ), v 4 = (2, 6, 18, 8, 6) We use the vectors form a matrix, A =

23 By using elementary row operations, it reduces to R = The 1st, 2nd, 3rd rows form a basis for the subspace spanned by v 1, v 2, v 3, v 4

24 Finding a basis for the row space of A = consisting entirely of row vectors from A

25 Basis for the row space of a matrix We know how to find a basis for the row space of a matrix by using the elementary row operations The basis vectors come from the row echelon form of the original matrix If we want to find the basis expressible by the original matrix We use A T A T =

26 By elementary row operations, So the 1st, 2nd, 4th columns of A T form a basis for the column space of A T That is to say, the 1st, 2nd, 4th rows of A form a basis for the row space of A

27 Homework and Reading Homework Ex # 1, # 2 (b) (d), # 3(b), # 4, # 5 (b), # 6(b) (c), # 7 (b), # 9, #1 (c) True or false questions on page 237 Reading Section 48