# 18.06 Problem Set 4 Solution Due Wednesday, 11 March 2009 at 4 pm in Total: 175 points.

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1 806 Problem Set 4 Solution Due Wednesday, March 2009 at 4 pm in 2-06 Total: 75 points Problem : A is an m n matrix of rank r Suppose there are right-hand-sides b for which A x = b has no solution (a) What are all the inequalities (< or ) that must be true between m, n, and r? (b) A T y = 0 has solutions other than y = 0 Why must this be true? Solution (5 points = 0+5) (a) First of all, the rank r of a matrix is the number of column (row) pivots, it must be less than equal to m and n If the matrix were of full row rank, ie, r = m, it would imply that A x = b always has a solution; we know that this is not the case, and hence r m To sum up, the inequalities among m, n, r are r n, r < m (b) Since A T is an n m matrix, the null space N(A T ) has dimension m r, which is positive by (a) Hence, A T y = 0 has solutions other than y = 0 Problem 2: A is an m n matrix of rank r Which of the four fundamental subspaces are the same for: ( ) A (a) A and A ( ) ( ) A A A (b) and A A A ( ) ( ) A A A Explain why all three matrices A,, and must have the same rank r A A A Solution (20 points = 0+0) (a) Note that if we do invertible row operations on the matrix ( ) A kill the bottom A and get 0 ( ) A, we may A

2 Nullspace: Since the nullspace is invariant under row operations, the two matrices have the same nullspace Column space: Since the two matrices do not have the same number of rows, their column space must not be the same Row space: Since the row space is also invariant under row operations, the two matrices have the same row space ( ) A Left nullspace: The transpose of is ( A A T A T) Hence, the left nullspace is a subspace of R 2m However, the left nullspace is a subspace of R m They cannot be the same ( ) ( ) A A A 0 (b) Using invertible column operations, we can turn into ( ) A A A 0 A A The nullspace and the row space of are subspaces of R A A 2n, whereas the ( ) A nullspace and the row space of are subspaces of R A n They cannot be the same Since the column space and the left nullspace are invariant under column operations, the two matrices have the same column space and left nullspace REMARK: For an m n matrix of rank r, we have Fundamental space subspace of dimension Nullspace R n n r Column space R m r Row space R n r Left nullspace R m m r Problem 3: Find a basis for each of the four subspaces for A = Solution (25 points = 5(echelon form) ) We first write A as in row-reduced echelon form

3 The second and the fourth variables are the pivots The first, third, and the fifth variables are free variables The row operation matrix E (R = EA) is E = Nullspace: The null space is as follow: 0 0 N(A) = x x x = for x, x 3, x 5 R Column space: Since the pivot columns (the second and the fourth) of A span the column space, we have 3 C(A) = a + b 4 for a, b R 0 CAUTION: We get the pivot columns by looking at R but C(A) is spanned by these columns of A, NOT these columns of R Row space: it is the same as the row space of R as R is obtained by invertible row operations So 0 0 C(A T ) = x x for x 2, x 4 R 2 2 Left nullspace: It has a basis given by the rows of E for which the corresponding rows of R are all zero That is to say, we need to take the last row of E Thus, N(A T ) = a for a R Problem 4: True or false (give a reason if true, or a counterexample if false): 3

4 (a) A and A T have the same number of pivots (b) A and A T have the same left nullspace (c) If the C(A) = C(A T ), then A = A T (d) If A T = A, then the row space of A is the same as the column space of A Solution (20 points = ) (a) True, because A and A T have the same rank, which equals to the number of pivots of the matrices (b) False In particular, if A is an m n matrix of rank r with m n, the dimension of two left nullspace will not be the same; Indeed, dim N(A T ) = m r and dim N ( (A T ) T) = n r For example, A = ( ( 0) ) The left nullspace of A is N(A T ) = 0 and the left null 0 space of A T is N(A) = a for a R (c) This is not necessarily true In particular, if A is a non-symmetric n n invertible matrix, C(A) = C(A T ) = R n (d) True, because the row space of A is C(A T ) = C( A) = C(A) is the same as the column space of A Problem 5: Use the Matlab command A = rand(0,5); to make a random 0 5 matrix A, and B = rand(5,9) to make a random 5 9 matrix B Then use the command [R,p] = rref(a*b); to find the row-reduced echelon form R and a list p of the pivot columns Using this information, give bases for the nullspace, column space, and row space of AB Solution (0 points) >> A = rand(0, 5) A =

5 >> B = rand(5, 9) B = >> A*B ans = >> [R,p] = rref(a*b) R =

6 p = The pivot variables are x,, x 5 and free variables x 6,, x 9 The column space is spanned by the first 5 columns of AB The row space of AB is the same as the row space of R which is generated by the first five rows of R The nullspace is given by the negative of the upper right 5 4 block together with a 4 4 identity matrix, one on top of the other In other words, it is the column space of the following matrix Problem 6: Explain why the following statement must be true: if a subspace S is contained in another subspace V, then the orthogonal complement V is contained in the orthogonal complement S Solution (5 points) Let x V be an element in the orthogonal complement We want to show that it is in the orthogonal complement S ; that is to say for any s S, x T s = 0 But we know that x T v = 0 for any v V and in particular, s is an element in a subspace of V So, x T s = 0 We are done 6

7 REMARK: Since the orthogonal complement of a subspace S is defined to be the subspace whose vectors pairs to zero with the vectors in S, the intuition here is that the larger the S is, the more restriction S has, and hence the smaller S is Problem 7: If A T A x = 0 then A x = 0 Reason: A T A x = 0 means that A x in the nullspace of A x is also in the space of A These two spaces are, so their only intersection is A x = 0 Thus, A T A has the same nullspace as A (We derive this in another way in class) Solution (5 points) A T ; column space; orthogonal Problem 8: Suppose you have two matrices V and W such that C(V ) and C(W ) are orthogonal subspaces What is V T W? Solution (5 points) It has to be zero Indeed, if we write V = ( v v n ) and W = ( w w m ), v i C(V ) and w j C(W ) and v T w v T w m V T W = v n T w v n T w m because C(V ) and C(W ) are orthogonal = Problem 9: Suppose L is a one-dimensional subspace (a line through the origin) in R 3 Its orthogonal complement L is the perpendicular to L Then (L ) is a perpendicular to L, and in fact (L ) is the same as Solution (5 points) plane; this is because dim L + dim L = 3 line; this is because dim L + dim(l ) = 3 L It is true in general that for a subspace V of R n, (V ) = V Problem 0: Let N be a matrix whose columns are a basis for the nullspace of A Then the nullspace of N T is the space of A 7

8 Solution (5 points) row The condition implies that the nullspace of A is the same as the column space of N, ien(a) = C(N) We also know that the null space of N T (or the left nullspace of N) is the orthogonal complement of the column space of N, ie N(N T ) = C(N), and the row space of A is the orthogonal complement of the nullspace of A, ie C(A T ) = N(A) Hence, N(N T ) = C(N) = N(A) = C(A T ) REMARK: The condition that the columns of N form a basis is not necessary, we need only that the column space of N is the same as the nullspace of A REMARK2: For a matrix A, the column space is orthogonal to the left nullspace and the row space is orthogonal to the nullspace Problem : Let A be the matrix A = ( ) (a) Find the projection matrix P C onto C(A) (b) Find the projection matrix P R onto the row space of A (c) Compute P C AP R Explain your result (d) For any matrix A (not necessarily the one above), with P C and P R defined as the projection matrices onto A s column and row space respectively, conclude that you would get P C AP R = Solution (20 points = ) (a) The column space C(A) is spanned by the vector a = P C onto C(A) is obtained via dividing aa T by a T a, which is ( ) 3 (3 ) / 4 ( ) = 4 ( ) 9/25 2/25 2/25 6/25 ( ) 3 So the projection 4 3 (b) The row space C(A T ) is spanned by the vector b = 6 So the projection 6 8

9 P R onto C(A T ) is bb T/ 3 b T b = 6 ( ) / /9 2/9 2/9 ( ) = 2/9 4/9 4/9 6 2/9 4/9 4/9 (c) P C AP R = = ( ) ( ) 2/9 2/9 9/25 2/ /9 2/9 4/9 4/9 2/25 6/ /9 4/9 4/9 ( ) 2/9 2/9 /9 2/9 4/9 4/9 = 2/9 4/9 4/9 ( ) = A See the explanation below (d) A This consists of two parts: P C A = A and AP R = A First, multiplying the projection matrix P C on the left is equivalent to projecting each column of A onto the subspace C(A) This will just give back A itself Thus, P C A = A Similarly, multiplying the projection matrix P R on the right is equivalent to projecting each row of A onto the subspace C(A T ) Thus, we will get back to A An alternative way to see this is to check it for each vector x We can write any vector x as x = P R x+z, where z is in the complement of the row space, which means that x is in the null space Therefore Ax = A(P R x + z) = AP R x + Az = AP R x since Az = 0, and hence A = AP R since this is true for all x Problem 2: Find the projection matrix P onto the plane x + 2y z = 0 in two ways: (a) Choose two vectors in the plane and make them the columns of a matrix A The plane is the column space Then compute P = A(A T A) A T (b) Write a vector e that is perpendicular to that plane Compute the matrix Q = e e T / e T e that projects onto the e direction Then compute P = I Q 9

10 Solution (20 points = 0+0) (a) We view the plane as thenull space of the matrix ( 2 ) Then a basis 2 of this space is given by, 0 Hence we take the matrix A to be 0 2 A = 0 0 Thus, A T A = ( ) 2 0 = 0 (A T A) = ( ) 5 2 and 2 2 ( ) /3 /3 /3 5/6 Then the projection matrix is 2 ( ) ( ) 5/6 /3 /6 P = A(A T A) A T = 0 /3 /3 2 0 = /3 /3 /3 /3 5/6 0 0 /6 /3 5/6 (b) Since the row space of the matrix ( 2 ) is orthogonal to the nullspace, which is the plane as explained in part (a), we may take e = 2 Thus, we have Q = ee T/ 2 / /6 /3 /6 e T e = = /3 2/3 /3 2 /6 /3 /6 Hence, 5/6 /3 /6 P = I Q = /3 /3 /3 /6 /3 5/6 Problem 3: The nullspace of A T is to the column space C(A), so if A T b = 0 then the projection of b onto C(A) should be p = Check that P b gives this answer, where P is the projection matrix P = A(A T A) A T 0

11 Solution (0 points) orthogonal 0; the condition says that b is in the orthogonal complement of the column space C(A) and hence it has projection 0 on that space We calculation P b as follows P b = A(A T A) A T b = A(A T A) 0 = 0 Problem 4: Explain why one must have P 2 = P, from the definition of the projection matrix P onto the column space of a matrix A (if we take a vector b and project it to the column space to get P b, then project it again, we must get ) Check explicitly that this is true from the formula P = A(A T A) A T Solution (0 points) P b; this is because we can write P 2 b as P (P b) and the projection restricted to the column space of A is identity, so further composition of P b with P will still give P b If P = A(A T A) A T, we have P b = A(A T A) A T b P b = ( A(A T A) A T) 2 b = A(A T A) A T A(A T A) A T b ( ) = A (A T A) (A T A) (A T A) A T b = A I (A T A) A T b = P b

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