# MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix.

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1 MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix.

2 Basis Definition. Let V be a vector space. A linearly independent spanning set for V is called a basis. Theorem A nonempty set S V is a basis for V if and only if any vector v V is uniquely represented as a linear combination v = r 1 v 1 +r 2 v 2 + +r k v k, where v 1,...,v k are distinct vectors from S and r 1,...,r k R. Remark on uniqueness. Expansions v = 2v 1 v 2, v = v 2 +2v 1, and v = 2v 1 v 2 +0v 3 are considered the same.

3 Dimension Theorem 1 Any vector space has a basis. Theorem 2 If a vector space V has a finite basis, then all bases for V are finite and have the same number of elements. Definition. The dimension of a vector space V, denoted dimv, is the number of elements in any of its bases. Examples. dimr n = n M m,n (R): the space of m n matrices; dimm m,n = mn P n : polynomials of degree less than n; dimp n = n P: the space of all polynomials; dimp = {0}: the trivial vector space; dim{0} = 0

4 How to find a basis? Theorem Let V be a vector space. Then (i) any spanning set for V contains a basis; (ii) any linearly independent subset of V is contained in a basis. Approach 1. Given a spanning set for the vector space, reduce this set to a basis. Approach 2. Given a linearly independent set, extend this set to a basis. Approach 2a. Given a spanning set S 1 and a linearly independent set S 2, extend the set S 2 to a basis adding vectors from the set S 1.

5 Problem. Find a basis for the vector space V spanned by vectors w 1 = (1,1,0), w 2 = (0,1,1), w 3 = (2,3,1), and w 4 = (1,1,1). To pare this spanning set, we need to find a relation of the form r 1 w 1 +r 2 w 2 +r 3 w 3 +r 4 w 4 = 0, where r i R are not all equal to zero. Equivalently, r 1 r 2 r 3 r 4 0 = 0. 0 To solve this system of linear equations for r 1,r 2,r 3,r 4, we apply row reduction.

6 (reduced row echelon form) r 1 +2r 3 = 0 r 2 +r 3 = 0 r 4 = 0 r 1 = 2r 3 r 2 = r 3 r 4 = 0 General solution: (r 1,r 2,r 3,r 4 )=( 2t, t,t,0), t R. Particular solution: (r 1,r 2,r 3,r 4 ) = (2,1, 1,0).

7 Problem. Find a basis for the vector space V spanned by vectors w 1 = (1,1,0), w 2 = (0,1,1), w 3 = (2,3,1), and w 4 = (1,1,1). We have obtained that 2w 1 +w 2 w 3 = 0. Hence any of vectors w 1,w 2,w 3 can be dropped. For instance, V = Span(w 1,w 2,w 4 ). Let us check whether vectors w 1,w 2,w 4 are linearly independent: = = = 1 0. They are!!! Thus {w 1,w 2,w 4 } is a basis for V. Moreover, it follows that V = R 3.

8 Row space of a matrix Definition. The row space of an m n matrix A is the subspace of R n spanned by rows of A. The dimension of the row space is called the rank of the matrix A. Theorem 1 The rank of a matrix A is the maximal number of linearly independent rows in A. Theorem 2 Elementary row operations do not change the row space of a matrix. Theorem 3 If a matrix A is in row echelon form, then the nonzero rows of A are linearly independent. Corollary The rank of a matrix is equal to the number of nonzero rows in its row echelon form.

9 Theorem Elementary row operations do not change the row space of a matrix. Proof: Suppose that A and B are m n matrices such that B is obtained from A by an elementary row operation. Let a 1,...,a m be the rows of A and b 1,...,b m be the rows of B. We have to show that Span(a 1,...,a m ) = Span(b 1,...,b m ). Observe that any row b i of B belongs to Span(a 1,...,a m ). Indeed, either b i = a j for some 1 j m, or b i = ra i for some scalar r 0, or b i = a i +ra j for some j i and r R. It follows that Span(b 1,...,b m ) Span(a 1,...,a m ). Now the matrix A can also be obtained from B by an elementary row operation. By the above, Span(a 1,...,a m ) Span(b 1,...,b m ).

10 Problem. Find the rank of the matrix A = Elementary row operations do not change the row space. Let us convert A to row echelon form:

11 Vectors (1,1,0), (0,1,1), and (0,0,1) form a basis for the row space of A. Thus the rank of A is 3. It follows that the row space of A is the entire space R 3.

12 Problem. Find a basis for the vector space V spanned by vectors w 1 = (1,1,0), w 2 = (0,1,1), w 3 = (2,3,1), and w 4 = (1,1,1). The vector space V is the row space of a matrix According to the solution of the previous problem, vectors (1,1,0), (0,1,1), and (0,0,1) form a basis for V.

13 Column space of a matrix Definition. The column space of an m n matrix A is the subspace of R m spanned by columns of A. Theorem 1 The column space of a matrix A coincides with the row space of the transpose matrix A T. Theorem 2 Elementary row operations do not change linear relations between columns of a matrix. Theorem 3 Elementary row operations do not change the dimension of the column space of a matrix (however they can change the column space). Theorem 4 If a matrix is in row echelon form, then the columns with leading entries form a basis for the column space. Corollary For any matrix, the row space and the column space have the same dimension.

14 Problem. Find a basis for the column space of the matrix A = The column space of A coincides with the row space of A T. To find a basis, we convert A T to row echelon form: A T = Vectors (1,0,2,1), (0,1,1,0), and (0,0,0,1) form a basis for the column space of A.

15 Problem. Find a basis for the column space of the matrix A = Alternative solution: We already know from a previous problem that the rank of A is 3. It follows that the columns of A are linearly independent. Therefore these columns form a basis for the column space.

16 Problem. Let V be a vector space spanned by vectors w 1 = (1,1,0), w 2 = (0,1,1), w 3 = (2,3,1), and w 4 = (1,1,1). Pare this spanning set to a basis for V. Alternative solution: The vector space V is the column space of a matrix B = The row echelon form of B is C = Columns of C with leading entries (1st, 2nd, and 4th) form a basis for the column space of C. It follows that the corresponding columns of B (i.e., 1st, 2nd, and 4th) form a basis for the column space of B. Thus {w 1,w 2,w 4 } is a basis for V.

17 Nullspace of a matrix Let A = (a ij ) be an m n matrix. Definition. The nullspace of the matrix A, denoted N(A), is the set of all n-dimensional column vectors x such that Ax = 0. a 11 a 12 a a 1n a 21 a 22 a a 2n..... a m1 a m2 a m3... a mn x 1 x 2 x 3. x n 0 = 0. 0 The nullspace N(A) is the solution set of a system of linear homogeneous equations (with A as the coefficient matrix).

18 Let A be an m n matrix. Then the nullspace N(A) is the solution set of a system of linear homogeneous equations in n variables. Theorem The nullspace N(A) is a subspace of the vector space R n. Definition. The dimension of the nullspace N(A) is called the nullity of the matrix A.

19 Problem. Find the nullity of the matrix ( ) A = Elementary row operations do not change the nullspace. Let us convert A to reduced row echelon form: ( ) ( ) ( ) { { x1 x 3 2x 4 = 0 x1 = x 3 +2x 4 x 2 +2x 3 +3x 4 = 0 x 2 = 2x 3 3x 4 General element of N(A): (x 1,x 2,x 3,x 4 ) = (t +2s, 2t 3s,t,s) = t(1, 2,1,0)+s(2, 3,0,1), t,s R. Vectors (1, 2,1,0) and (2, 3,0,1) form a basis for N(A). Thus the nullity of the matrix A is 2.

20 rank + nullity Theorem The rank of a matrix A plus the nullity of A equals the number of columns in A. Sketch of the proof: The rank of A equals the number of nonzero rows in the row echelon form, which equals the number of leading entries. The nullity of A equals the number of free variables in the corresponding system, which equals the number of columns without leading entries in the row echelon form. Consequently, rank+nullity is the number of all columns in the matrix A.

21 Problem. Find the nullity of the matrix ( ) A = Alternative solution: Clearly, the rows of A are linearly independent. Therefore the rank of A is 2. Since (rank of A) + (nullity of A) = 4, it follows that the nullity of A is 2.

22 Theorem Suppose A and B are matrices of the same dimensions. Then the following conditions are equivalent: (i) A can be obtained from B by applying elementary row operations; (ii) A = CB for an invertible matrix C; (iii) A and B share a row echelon form; (iv) A and B have the same reduced row echelon form; (v) A and B have the same row space; (vi) A and B have the same nullspace.

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