Chapter4 - Chapter 4 Vector Spaces Associated with Matrices Section 4.1 Row Spaces and Column Spaces Discussion 4.1.1 Each m × n matrix is

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Chapter 4 Vector Spaces Associated with Matrices Section 4.1 Row Spaces and Column Spaces Discussion 4.1.1 Each m × n matrix is naturally associated with three vector spaces, namely, the row space, the column space and the nullspace. These three vector spaces provide us with insights into the relationships between solutions of linear systems and the coefficient matrix. In the following, we shall first discuss the row and column spaces of a matrix. Definition 4.1.2 Let A = ( a ij ) be an m × n matrix, i.e. A =      a 11 a 12 ··· a 1 n a 21 a 22 ··· a 2 n . . . . . . . . . a m 1 a m 2 ··· a mn      . The row space of A is the subspace of R n spanned by the rows of A . The column space of A is the subspace of R m spanned by the columns of A . Let r 1 , r 2 , ··· , r m be the m rows of A , i.e. r 1 = ( a 11 ,a 12 ,...,a 1 n ) , r 2 = ( a 21 ,a 22 ,...,a 2 n ) , . . . r m = ( a m 1 ,a m 2 ,...,a mn ) , and let c 1 , c 2 , ··· , c n be the n columns, i.e. 2 Chapter 4. Vector Spaces Associated with Matrices c 1 =      a 11 a 21 . . . a m 1      , c 2 =      a 12 a 22 . . . a m 2      , ··· , c n =      a 1 n a 2 n . . . a mn      . Then the row space of A = span { r 1 , r 2 , ··· , r m } and the column space of A = span { c 1 , c 2 , ··· , c n } . Remark 4.1.3 The row space of A is the same as the column space of A T while the column space of A is the same as the row space of A T . Example 4.1.4 1. Consider the matrix A =     2- 1 0 1- 1 3- 5 1 1 1     . The rows of A are r 1 = (2 ,- 1 , 0) r 2 = (1 ,- 1 , 3) r 3 = (- 5 , 1 , 0) r 4 = (1 , , 1) and its columns are c 1 =     2 1- 5 1     , c 2 =    - 1- 1 1     , c 3 =      3 1      . Note that the row space of A is a subspace of R 3 while the column space of A is a subspace of R 4 . 2. Find a basis for the row space and a basis for the column space of A given in Part 1. Hence state the dimension of the row space and the dimension of the column space of A . Solution (In Remark 4.1.9 and Remark 4.1.13, we have methods for finding bases for row and column spaces of matrices. For this example, we just do it by brute Section 4.1. Row Spaces and Column Spaces 3 force.) We first consider the row space of A . The row space of A is a subspace of R 3 and hence any basis of the row space of A contains at most three vectors. Since the rows r 1 , r 2 and r 3 of A are linearly independent (check it), we see that they form a basis for the row space of A . All three columns c 1 , c 2 and c 3 of A are linearly independent (check it) and so form a basis for the column space of A ....
View Full Document

This note was uploaded on 01/24/2011 for the course SPMS MAS 114 taught by Professor Profdavidh.adams during the Spring '10 term at Nanyang Technological University.

Page1 / 13

Chapter4 - Chapter 4 Vector Spaces Associated with Matrices Section 4.1 Row Spaces and Column Spaces Discussion 4.1.1 Each m × n matrix is

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online