# 4.6 Null Space, Column Space, Row Space

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1 NULL SPACE, COLUMN SPACE, ROW SPACE Null Space, Column Space, Row Space In applications of linear algebra, subspaces of R n typically arise in one of two situations: ) as the set of solutions of a linear homogeneous system or ) as the set of all linear combinations of a given set of vectors In this section, we will study, compare and contrast these two situations We will nish the section with an introduction to linear transformations The Null Space of a Matrix De nitions and Elementary Remarks and Examples In previous section, we have already seen that the set of solutions of a homogeneous linear system formed a vector space (theorem ) This space has a name De nition The null space of an m n matrix A, denoted Null A, is the set of all solutions to the homogeneous equation Ax = Written in set notation, we have Null A = fx : x R n and Ax = g Remark As noted earlier, this is a subspace of R n In particular, the elements of Null A are vectors in R n if we are working with an m n matrix Remark We know that Null A = since it always contain, the trivial solution The question is can it have nontrivial solutions? Example The elements of Null A if A is are vectors of R Example The elements of Null A if A is are vectors in R Example The elements of Null A if A is are vectors in R Example 8 The elements of Null A if A is are vectors in R Remark 9 The kind of elements Null A contains (which vector space they belong to) depends only on the number of columns of A We now look at speci c examples and how to nd the null space of a matrix Examples Usually, when one is trying to nd the null space of a matrix, one tries to nd a basis for it So, when asked to " nd the null space" of a matrix, one is asked to nd a basis for it The examples below illustrate how to do this

2 8 CHAPTER VECTOR SPACES Example Find Null A for A = 8 First, let us remark that the elements of Null A will be elements of R We are nding the solution set of Ax = The augmented matrix of the system is, the reduced row-echelon form is: 8 8 x = r + s t x = r The solutions of the system are x = s + t x = s x = t x x x x = r + s + t x by fu v wg where u =, v = which can be written as Null A is the subspace spanned and w = It should be clear that this set is also linearly independent So, it is a basis for Null 8A Hence, Null Ahas dimension 9 and it is the subspace of R with basis >= > Example Find Null A for A = First, let us remark that the elements of Null A will be elements of R To nd Null A, we solve the system Ax = The augmented matrix is Its reduced row-echelon form is So, the solutions are

3 NULL SPACE, COLUMN SPACE, ROW SPACE 9 8 < : t x = t s x = t that is x = s x = which can be written as x = s + x = s 8 Thus Null A is a subspace of R, of dimension with basis x + x + x = x + x = x = 9 >= > Additional Theoretical Results If should be clear to the reader that if A is invertible then Null A = fg Indeed, if A is invertible, then Ax = only has the trivial solution We state it as a theorem Theorem If A is invertible then Null A = fg In earlier chapters, we developed the technique of elementary row transformations to solve a system In particular, we saw that performing elementary row operations did not change the solutions of linear systems We state this result as a theorem Theorem Elementary row operations on a matrix A do not change Null A De nition The nullity of a matrix A, denoted nullity (A) is the dimension of its null space Example From the previous examples, we see that if A = then nullity (A) = and if B = 8 then nullity (B) = Column Space and Row Space of a Matrix De nitions and Elementary Concepts Let us start by formalizing some terminology we have already used

4 CHAPTER VECTOR SPACES De nition Consider an m n matrix a a a a n a a a a n a a a a n a m a m a m a mn The vectors r = a a a :::, r m = a m a m a m a mn a n, r = a a a in R n obtained from the rows a n, of A are called the row vectors of A The vectors c = a a a a m, c = a a a a m, :::, c n = a n a n a n a mn in R m obtained from the columns of A are called the column vectors of A We now turn to the main de nitions of this section De nition Let A be an m n matrix The subspace of R n spanned by the row vectors of A is called the row space of A The subspace of R m spanned by the column vectors of A is called the column space of A In this subsection, we will try to answer the following questions: How does one nd the row space of a matrix A? How does one nd the column space of a matrix A? Is there a relationship between them? Is there a relationship between them and the null space of a matrix A? Since matrices are closely related to solving systems of linear equations, is there a relationship between the row space, column space, null space of a matrix A and the linear system Ax = b? With the notation already introduced, and letting x = we can write Ax = x c + x c + ::: + x n c n x x x n we see that Thus, solving Ax = b amounts to solving x c +x c +:::+x n c n = b So, we see that for the system to have a solution, b must be in the span of fc c ::: c n g We state this a a theorem

5 NULL SPACE, COLUMN SPACE, ROW SPACE Theorem 8 A system of linear equations Ax = b is consistent if and only if b is in the column space of A We now look at some important results about the column space and the row space of a matrix Theoretical Results First, we state and prove a result similar to one we already derived for the null space Theorem 9 Elementary row operations do not change the row space of a matrix A Proof Suppose that A is m n with rows r r n ::: r m Let B be obtained from A by one of the elementary row operations Suppose that the rows of B are r r n ::: r m We divide the proof according to the kind of elementary row transformation being applied We do the proof by showing that every vector in the row space of B is in the row space of A and vice-versa In other words, we prove inclusion both ways a standard technique to show two sets are equal Row interchange This case is easy A and B will still have the same row space since they will have the same rows Replacing a row by a multiple of another or by itself plus a multiple of another This can be generalized by saying that one or more of r i are linear combinations of the r j s Thus the vectors r r n ::: r m lie in the row space of A Since a vector space is closed under linear combinations, any linear combination of r r n ::: r m will also be in the row space of A But that s precisely what the row space of B is, linear combinations of r r n ::: r m Thus the row space of B is in the row space of A Since every elementary row transformation has an inverse transformation, we can transform B into A Using the same argument as above, we will prove that the row space of A in in the row space of B Remark This result, unfortunately, does not apply to the column space of A Only its row space is preserved under elementary row operations However, we do have the following results: The next two theorems are given without proof Theorem Let A and B be two matrices which are row equivalent (one is obtained from the other with elementary row operations) A given set of column vectors of A is linearly independent if and only if the corresponding column vectors of B are linearly independent A given set of column vectors of A form 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

6 CHAPTER VECTOR SPACES The following theorem makes it easy to nd a basis for the row and column space of a matrix We will use it in the examples Theorem If a matrix R is in row-echelon form then: The row vectors with the leading s form a basis for the row space of R The column vectors with the leading s of the row vectors form a basis for the column space of R Let us make some remarks about this theorem Remark Combining theorems and, we see that to nd a basis for the row space of a matrix of a matrix, we put it in row-echelon form and extract the row vectors with a leading Remark Since row elementary row operations do not preserve the column space, to nd the column space of a matrix will be a little more di cult But not too much Let A be a matrix and B be the row-echelon form of A If we call c c ::: c n the columns of A and c c ::: c n the columns of B We look at the columns of B which have a leading of the rows of B A basis for the column space of A will be the vectors c i for the values of i for which c i has a leading of the rows of B This sounds complicated but it is not We will illustrate this with examples Finally, we give a theorem which relates the null space of A and solutions of Ax = b Theorem If x is any solution of Ax = b and v v ::: v r form a basis for the null space of A, then any solution of Ax = b can be written as x = x + c v + c v + ::: + c r v r () and conversely, any vector written this way for any scalars c c ::: c r is a solution of Ax = b Proof We prove both ways Assume x is any solution of Ax = b and v v ::: v r form a basis for the null space of A We must prove that any solution of Ax = b can be written as x = x + c v + c v + ::: + c r v r If x is any solution of Ax = b then we have Ax = b and Ax = b that is Ax = Ax or A (x x ) = Thus, x x is in the null space of A, that is there exists scalars c c ::: c r such that x x = c v + c v + ::: + c r v r or x = x + c v + c v + ::: + c r v r Conversely, suppose that for any scalars c c ::: c r, x = x + c v + c v + ::: + c r v r, where x is a solution of Ax = b we must show that x is a solution of Ax = b Ax = (x + c v + c v + ::: + c r v r ) = Ax + c Av + c Av + ::: + c r Av r = b ::: + since the vi s are a basis for Null A Thus we see that Ax = b

7 NULL SPACE, COLUMN SPACE, ROW SPACE We now see how these results help us nding the column space and the row space of a matrix Examples We begin with very easy examples Example Consider the matrix in row-echelon form A = Find a basis for the row space and column space Since the matrix is in row-echelon form, we can apply theorem directly Row Space: A basis is the set of row vectors with a leading, that is Hence the row space has dimension 8 Column Space: A basis is space has dimension 9 >= Hence the column > Example Find bases for the row space and column space of A = We begin by nding the row-echelon form of A It is Row Space: A basis is the set of row vectors with a leading, that is Hence the row space has dimension Column Space: The columns with a leading from the row vectors are,, and Hence, a basis for the8column space arecolumns, 9, and from the original matrix that is >= 9 Hence > the column space has dimension Remark 8 In the two examples above, we see that the row space and column space have the same dimension This was not an accident We will see that indeed they do have the same dimension

8 CHAPTER VECTOR SPACES Application to Linear Transformations A transformation is the linear algebra term of a concept studied in calculus which was then called a function Another di erence is that a transformation works on vectors Its input values are vectors, its output values are also vectors The input and output values may not be from the same vector space Like functions, a transformation assigns to each input value a unique output value In this section, we only look at a special kind of transformations called linear transformations We now give a precise mathematical de nition De nition 9 (Linear Transformation) A linear transformation T from a vector space V to another vector space W is a rule which assigns to each vector x in V a unique vector T (x) in W, such that: T (u + v) = T (u) + T (v) for every u and v in V T (cu) = ct (u) for every u in V and every scalar c Remark The two conditions which have to be satis ed is what make the transformation a linear transformation Remark If T is a transformation from V to W, we often write T : V! W Let us look at some linear transformations we already know Example Let A be an mn matrix De ne T : R n! R m by T (x) = Ax It is easy to see that T is a linear transformation from the properties of matrices and T (x + x ) = A (x + x ) = Ax + Ax = T (x) + T (x ) T (cx) = A (cx) = cax = ct (x) Example Let V be the vector space of real valued functions with continuous rst derivatives de ned on an interval [a b] and let W be the space of continuous functions on [a b] Consider the transformation D : V! W de ned by D (f) = f It is easy to see that D is indeed a linear transformation D (f + g) = (f + g) = f + g (sum rule for di erentiation) = D (f) + D (g)

9 NULL SPACE, COLUMN SPACE, ROW SPACE and D (cf) = (cf) = cf = cd (f) When dealing with linear transformation, there are two concepts of importance, de ned below De nition Let T : V! W be a linear transformation The kernel of T, denoted ker (T ) is the set of vectors v in V such that T (v) = The range of T is the set of all vectors in W of the form T (x) for some x in V Example If the linear transformation is as in example, then ker (D) is the set of functions f such that D (f) = that is f = So, it is the set of constant functions Remark If T is as in example, then the kernel of T is simply the null space of A and the range of T is simply the column space of A Example Let T : R! R be de ned by T (x) = Ax where A = Find the kernel and the range of T Kernel of T : As noticed in the remark, nding the kernel of T amounts to nding the null space of A So, weneed to solve the system Ax = The augmented matrix of the system is Its reduced row-echelon form is 8 < the solutions are : 8, or written in paramet- ric form: x x x x = x + x + x = x + x = x = s + t + u x = s x = t u x = t x = u x = u or x x x x x x = s Thus, + t +

10 CHAPTER VECTOR SPACES u So, we see that the dimension of ker (T ) is Range of T : As noticed in the remark, it is the column space of A We already computed it in a8 previous example and found 9that it was the subspace of R with basis >= 9 Hence the range > of T has dimension Problems Do #,, a, b, c,,, 8, 9,,, on pages, 8 Suppose that T : V! W is a linear transformation (a) Prove that ker (T ) is a subspace of V (b) Prove that the range of T is a subspace of W

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