1. How does matrix algebra differ from the "Usual" algebra of(Answered)
Description
Instant Solution ? Click "Buy button" to Download the solution File
Question
1. How does matrix algebra differ from the "Usual" algebra of numbers. Give several examples to demonstrate some of the differences. For example, you might find two matrices and which do not have the commutative property for multiplication.
2. What similarities and differences are there between matrix algebra and other algebras we have seen in this course, namely the algebra of sets and the algebra of logic. Again, there are some similarities and some differences. Give concrete examples. You may find it useful to read scetion 5.6 and consider the supplemental exercises from section 5.6.
chapter 5
INTRODUCTION TO MATRIX ALGEBRA
GOALS
The purpose of this chapter is to introduce you to matrix algebra, which has many applications. You are already familiar with several algebras:
elementary algebra, the algebra of logic, the algebra of sets. We hope that as you studied the algebra of logic and the algebra of sets, you
compared them with elementary algebra and noted that the basic laws of each are similar. We will see that matrix algebra is also similar. As in
previous discussions, we begin by defining the objects in question and the basic operations.
5.1 Basic Definitions
Definition: Matrix. A matrix is a rectangular array of elements of the form
A =
a11
a21
a31
?
am1
a12
a22
a32
?
am2
a13
a23
a33
?
am3
??
??
??
??
??
a1 n
a2 n
a3 n
?
amn
A convenient way of describing a matrix in general is to designate each entry via its position in the array. That is, the entry a34 is the entry in
the third row and fourth column of the matrix A. Depending on the situation, we will decide in advance to which set the entries in a matrix will
belong. For example, we might assume that each entry aij (1 ? i ? m, 1 ? j ? n) is a real number. In that case we would use Mm?n HRL to
stand for the set of all m by n matrices whose entries are real numbers. If we decide that the entries in a matrix must come from a set S, we use
Mm?n HSL to denote all such matrices.
Definition: Order of a Matrix. The matrix A that has m rows and n columns is called an m ? n (read "m by n") matrix, and is said to
have order m ? n.
Since it is rather cumbersome to write out the large rectangular array above each time we wish to discuss the generalized form of a matrix, it is
common practice to replace the above by A = Aaij E. In general, matrices are often given names that are capital letters and the corresponding
lower case letter is used for individual entries. For example the entry in the third row, second column of a matrix called C would be c32 .
Example 5.1.1.
2 3
A =K
O,B =
0 5
0
1 2 5
6 2 3
,
and
D
=
2
4 2 8
15
1
are 2 ? 2, 3 ? 1, and 3 ? 3 matrices, respectively
Since we now understand what a matrix looks like, we are in a position to investigate the operations of matrix algebra for which users have
found the most applications.
Example 5.1.2. First we ask ourselves: Is the matrix A = K
the matrix B = K
1 2
O equal to
3 4
1 2
O? No, they are not because the corresponding entries in the second row, second column of the two matrices are not
3 5
1 2 3
1 2
equal. Next, is A = K
O equal to B = K
O? No, although the corresponding entries in the first two columns are identical, B doesn't
4 5 6
4 5
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
have a third column to compart to that of A. We formalize these observations in the following definition.
Definition; Equality of Matrices. A matrix A is said to be equal to matrix B (written A = B) if and only if:
(1) A and B have the same order, and
(2) all corresponding entries are equal: that is, aij = bij for all appropriate i and j.
5.2 Addition and Scalar Multiplication
Example 5.2.1. Concerning addition, it seems natural that if
A =K
1 0
3 4
1+3 0+4
4 4
O and B = K
O , then A + B = K
O=K
O.
2 1
2  5 1 + 2
5 2
3 1
However, if A = K
1 2 3
3 0
O and B = K
O, can we find A + B? No, the orders of the two matrices must be identical.
0 1 2
2 8
Definition: Matrix Addition. Let A and B be m ? n matrices. Then A + B is an m ? n matrix where HA + BLij = aij + bij (read "the ith
jth entry of the matrix A + B is obtained by adding the ith jth entry of A to the ith jth entry of B"). If the orders of A and B are not identical,
A + B is not defined.
It should be clear from Example 5.2.1 and the definition of addition that A + B is defined if and only if A and B are of the same order.
Another frequently used operation is that of multiplying a matrix by a number, commonly called a scalar in this context. Scalars normally come
from the same set as the entries in a matrix. For example, if A ? Mm?n HRL, a scalar can be any real number.
Example 5.2.2. If c = 3 and if A = K
3A =K
1 2
O and we wish to find c A, it seems natural to multiply each entry of A by 3 so that
3 5
3 6
O , and this is precisely the way scalar multiplication is defined.
9 15
Definition: Scalar Multiplication. Let A be an m ? n matrix and c a scalar. Then c A is the m ? n matrix obtained by multiplying c times
each entry of A; that is HcALij = c aij .
5.3 Multiplication of Matrices
 For a video introduction to this section, go to http://faculty.uml.edu/klevasseur/ads2/videos/matrixmultiplication/
A definition that is more awkward to motivate (and we will not attempt to do so here) is the product of two matrices. In time, the reader will see
that the following definition of the product of matrices will be very useful, and will provide an algebraic system that is quite similar to elementary algebra.
Definition: Matrix Multiplication. Let A be an m ? n matrix and let B be an n ? p matrix. The product of A and B, denoted by AB, is an
m ? p matrix whose ith row jth column entry is
HA BLi j = ai 1 b1 j + ai 2 b2 j + ?? + ai n bn j
= ai k bk j
n
k= 1
for 1 ? i ? m and 1 ? j ? p.
The mechanics of computing one entry in the product of two matrices is illustrated in Figure 5.3.1.
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
Figure 5.3.1 Computation of one entry in the product of two 3 by 3 matrices
The computation of a product can take a considerable amount of time in comparison to the time required to add two matrices. Suppose that A
and B are n ? n matrices; then HABLij is determined performing n multiplications and n  1 additions. The full product takes n3 multiplications
and n3 ? n2 additions. This compares with n2 additions for the sum of two n ? n matrices. The product of two 10 by 10 matrices will require
1,000 multiplications and 900 additions, clearly a job that you would assign to a computer. The sum of two matrices requires a more modest
100 additions. This analysis is based on the assumption that matrix multiplication will be done using the formula that is given in the definition.
There are more advanced methods that, in theory, reduce operation counts. For example, Strassen's algorithm
(http://mathworld.wolfram.com/StrassenFormulas.html) computes the product of two n by n matrices in 7 ? 7log2 n  6 ? 4log2 n ? 7 n2.808 operations. There are practical issues involved in actually using the algorithm in many situations. For example, roundoff error can be more of a
problem than with the standard formula.
Example 5.3.1. Let A =
AB =
Remarks:
1 0
6
3 2 , a 3 ? 2 matrix, an let B = K O, a 2 ? 1 matrix. Then A B is a 3 ? 1 matrix:
1
5 1
1 0
6
3 2 K O =
1
5 1
1?6 + 0?1
2?1 + 3 ?6
5 ? 6 + 1 ? 1
=
6
20
29
(1) The product A B is defined only if A is an m ? n matrix and B is an n ? p matrix; that is, the two "inner" numbers must be the equal.
Furthermore, the order of the product matrix A B is the "outer" numbers, in this case m ? p.
(2) It is wise to first determine the order of a product matrix. For example, if A is a 3 ? 2 matrix and B is a 2 ? 2 matrix, then A B is a 3 ? 2
matrix of the form
c11 c12
A B = c21 c22
c31 c32
Then to obtain, for example, C31 , we multiply corresponding entries in the third row of A times the first column of B and add the results.
Example 5.3.2.
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
Let A = K
1 0
3 0
O, and B = K
O . Then
0 3
2 1
AB =K
Note: B A = K
Remarks;
1?3 + 0?2 1?0 + 0?1
3 0
O= K
O
0?3 + 3?2 0?0 + 3?1
6 3
3 0
O ?? A B
2 3
(1) An n ? n matrix is called a square matrix.
(2) If A is a square matrix, A A is defined and is denoted by A2 , and A A A = A3 , etc.
(3) The m ? n matrices whose entries are all 0 are denoted by 0m?n , or simply 0, when no confusion arises regarding the order.
EXERCISES FOR SECTIONS 5.1 THROUGH 5.3
A Exercises
1. Let A = K
1 1
0 1
0 1 1
O, B = K
O , and C = K
O
2 3
3 5
3 2 2
(a) Compute A B and B A.
(b) Compute A + B and B + A.
(c) If c = 3, show that cHA + BL = c A + c B.
(d) Show that HA BL C = A HB CL.
(e) Compute A2 C.
(f) Compute B + 0
(g) Compute A 02?2 and 02?2 A, where 02?2 is the 2 ? 2 zero matrix,
(h) Compute 0 A, where 0 is the real number (scalar) zero.
(i) Let c = 2 and d = 3. Show that Hc + dL A = c A + d A.
2. Let A =
1 0 2
2 1 5
3 2 1
Compute, if possible;
,B =
0 2 3
2 1 2 3
1 1 2 , and C = 4 0 1 1
1 3 2
3 1 4 1
(a) A  B
(b) A B
(c) A C  B C
(d) A HB CL
(e) C A  C B
x
y
(f) C
z
w
3. Let A = K
2 0
1 0
O . Find a matrix B such that A B = I and B A = I, where I = K
O
0 3
0 1
4. Find A I and B I where I is as in Exercise 3, where
A = K
1 8
2 3
O and B = K
O.
9 5
5 7
What do you notice?
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
1 0 0
5. Find A3 if A = 0 2 0
0 0 3
. What is A15 equal to?
B Exercises
6. (a) Determine I 2 and I 3 if I =
1 0 0
0 1 0 .
0 0 1
(b) What is I n equal to for any n ? 1?
(c) Prove your answer to part (b) by induction.
7. (a) If A = K
x1
2 1
3
O, X = K O, and B = K O , show that A X = B is a way of expressing the system
x2
1 1
1
(b) Express the following systems of equations using matrices:
2 x1 + x2 = 3 using matrices.
x1  x2 = 1
(i) 2 x1  x2 = 4
x1 + x2 = 0
(ii)
x1 + x2 + 2 x3 = 1
x1 + 2 x2  x3 = 1
x1 + 3 x 2 + x 3 = 5
(iii) x1 + x2
= 3
x2
= 5
x1
+ 3 x3 = 6
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
5.4 Special Types of Matrices
We have already investigated one special type of matrix, namely the zero matrix, and found that it behaves in matrix algebra in an analogous
fashion to the real number 0; that is, as the additive identity. We will now investigate the properties of a few other special matrices.
Definition: Diagonal Matrix. A square matrix D is called a diagonal matrix if dij = 0 whenever i ?? j.
Example 5.4.1.
A =
1 0 0
0 2 0 , B=
0 0 5
3 0 0
0 0 0 , and I =
0 0 5
1 0 0
0 1 0
0 0 1
are all diagonal matrices.
In Example 5.4.1, the 3 ? 3 diagonal matrix I whose diagonal entries are all 1's has the singular property that for any other 3 ? 3 matrix A we
have A I = I A = A. For example:
Example 5.4.2. If A =
1 2 5
6 7 2 , then
3 3 0
1 2 5
A I = 6 7 2
3 3 0
IA =
and
1 2 5
6 7 2 .
3 3 0
In other words, the matrix I behaves in matrix algebra like the real number 1; that is, as a multiplicative identity. In matrix algebra the matrix I
is called simply the identity matrix. Convince yourself that if A is any n ? n matrix A I = I A = A.
Definition: Identity Matrix. The n ? n diagonal matrix whose diagonal components are all 1's is called the identity matrix and is
denoted by I or In .
In the set of real numbers we realize that, given a nonzero real number x, there exists a real number y such that x y = y x = 1. We know that
real numbers commute under multiplication so that the two equations can be summarized as x y = 1. Further we know that y = x1 =
1
. Do
we have an analogous situation in Mn?n HRL? Can we define the multiplicative inverse of an n ? n matrix A? It seems natural to imitate the
definition of multiplicative inverse in the real numbers.
Definition: Matrix Inverse. Let A be an n ? n matrix. If there exists an n ? n matrix B such that A B = B A = I, then B is the multiplicative inverse of A (called simply the inverse of A) and is denoted by A1 (read "A inverse").
x
When we are doing computations involving matrices, it would be helpful to know that when we find A1 , the answer we obtain is the only
inverse of the given matrix.
Remark: Those unfamiliar with the laws of matrices should go over the proof of Theorem 5.4.1 after they have familiarized themselves with the
Laws of Matrix Algebra in Section 5.5.
Theorem 5.4.1. The inverse of an n ? n matrix A, when it exists, is unique.
Proof: Let A be an n ? n matrix. Assume to the contrary, that A has two (different) inverses, say B and C. Then
B = BI
= B HA C L
= HB AL C
= IC
= C
Identity property of I
Assumption that C is an inverse of A
Associativity of matrix multiplication
Assumption that B is an inverse of A
Identity property of I
?
2 0
Example 5.4.3. Let A = K
O.
0 3
What is A
1
? Without too much difficulty, by trial and error, we determine that A
1
=
1
0
0
1
2
3
. This
might lead us to guess that the inverse is found by taking the reciprocal of all nonzero entries of a matrix. Alas, it isn't that easy!
A =K
1 2
O , the "reciprocal rule" would tell us that the inverse of A is B =
3 5
1
1
3
1
2
1
If
. Try computing A B and you will see that you don't get
5
the identity matrix. So, what is A1 ? In order to understand more completely the notion of the inverse of a matrix, it would be beneficial to
have a formula that would enable us to compute the inverse of at least a 2 ? 2 matrix. To do this, we need to recall the definition of the determinant of a 2 ? 2 matrix. Appendix A gives a more complete description of the determinant of a 2 ? 2 and higherorder matrices.
Definition: Determinant of a 2 ? 2 Matrix. Let A = K
a b
O. The determinant of A is the number det A = a d  b c.
c d
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
In addition to det A, common notation for the determinant of matrix A is A . This is particularly common when writing out the whole matrix,
a b
which case we would write
for the determinant of the general 2 ? 2 matrix.
c d
Example 5.4.4.
If A = K
If B = K
1 2
O then det A = 1 ? 5  2 ? H3L = 11.
3 5
1 2
O then det B = 1 ? 4  2 ? 2 = 0
2 4
Theorem 5.4.2. Let A = K
a b
O. If det A ?? 0, then A1 =
c d
Proof: See Exercise 4 at the end of this section.
1
det A
K
d b
O
c a
Example 5.4.5. Can we find the inverses of the matrices in Example 5.4.4?
1 2
If A = K
O then A1 =
3 5
1
11
5 2
K
O=
3 1
5
11
3
11

2
11
1
11
The reader should verify that A A1 = A1 A = I.
The second matrix, B has a determinant equal to zero. We we tried to apply the formula in Theorem 5.4.2, we would be dividing by zero.
For this reason, the formula can't be applied and in fact B1 does not exist.
Remarks:
(1) In general, if A is a 2 ? 2 matrix and if det A = 0, then A1 does not exist.
(2) A formula for the inverse of n ? n matrices n ? 3 can be derived that also involves det A, Hence, in general, if the determinant of a matrix is
zero, the matrix does not have an inverse. However the formula for even a 3 ? 3 matrix is very long and is not the most efficient way to
compute the inverse of a matrix.
(3) In Chapter 12 we will develop a technique to compute the inverse of a higherorder matrix, if it exists.
(4) Matrix inversion comes first in the hierarchy of matrix operations; therefore, A B1 is AHB1 L.
EXERCISES FOR SECTION 5.4
A Exercises
1. For the given matrices A find A1 if it exists and verify that A A1 = A1 A = I If A1 does not exist explain why.
(a) A = K
(b) A = K
1 3
O
2 1
6 3
O
8 4
(c) A = K
(d) A = K
1 3
O
0 1
1 0
O
0 1
(e) Use the definition of the inverse of a matrix to find A1 :
3 0
A= 0
1
2
0
0
0 0 5
2. For the given matrices A find A1 if it exists and verify that A A1 = A1 A = I If A1 does not exist explain why.
(a) A = K
2 1
O
1 2
(b) A = K
0 1
O
0 2
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
(c) A = K
(d) A = K
1 c
O
0 1
a b
O, were a > b > 0.
b a
3. (a) Let A = K
2 3
3 3
O and B = K
O. Verify that HA BL1 = B1 A1 .
1 4
2 1
(b) Let A and B be n ? n invertible matrices. Prove that HA BL1 = B1 A1 . Why is the right side of the above statement written "backwards"?
Is this necessary? Hint: Use Theorem 5.4.1.
B Exercises
4. Let Let A = K
a b
O. Derive the formula for A1 .
c d
5. (a) Let A and B be as in problem 3 above. Show that detHA BL = Hdet AL Hdet BL.
(b) It can be shown that the statement in part (a) is true for all n ? n matrices. Let A be any invertible n ? n matrix. Prove that
detHA1 L = Hdet AL1 . Note: The determinant of the identity matrix In is 1 for all n, see Appendix A for details.
(c) Verify that the equation in part (b) is true for the matrix in exercise l(a) of this section.
6. Prove by induction that for n ? 1, K
a 0 n
an 0
O = K
O.
0 b
0 bn
7. Use the assumptions in exercise 5 to prove by induction that if n ? 1, detHAn L = Hdet ALn .
8. Prove: If the determinant of a matrix A is zero, then A does not have an inverse. Hint: Use the indirect method of proof and exercise 5.
C Exercise
9. (a) Let A, B, and D be n ? n matrices. Assume that B is invertible. If A = B D B1 , prove by induction that Am = B Dm B1 is true for
m ? 1.
(b) Given that A = K
8 15
1 0
5 3
O B1 where B = K
O what is A10 ?
O = BK
0 2
3 2
6 11
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
5.5 Laws of Matrix Algebra
The following is a summary of the basic laws of matrix operations. Assume that the indicated operations are defined; that is, that the orders of
the matrices A, B, and C are such that the operations make sense.
(1) A + B = B + A
(2) A + HB + CL = HA + BL + C
(3) c HA + BL = c A + c B, where c ? R.
(4) Hc1 + c2 L A = c1 A + c2 A, where c1 , c2 ? R.
(5) c1 Hc2 AL = Hc1 ? c2 L A, where c1 , c2 ? R.
(6) 0 A = 0, where 0 is the zero matrix.
(7) 0 A = 0, where 0 on the left is the number 0.
(8) A + 0 = A.
(9) A + H1L A = 0.
(10) A HB + CL = A B + A C.
(11) HB + CL A = B A + C A.
(12) AHB CL = HA BL C.
(13) I A = A and A I = A.
(14) If A1 exists, HA1 L1 = A.
(15) If A1 and B1 exist, HA BL1 = B1 A1
Example 5.5.1. If we wished to write out each of the above laws more completely, we would specify the orders of the matrices. For
example, Law 10 should read:
(10) Let A, B, and C be m ? n, n ? p, and n ? p matrices, respectively, then A HB + CL = A B + A C
Remarks:
(1) Notice the absence of the "law" A B = B A. Why?
(2) Is it really necessary to have both a right (No. 11) and a left (No. 10) distributive law? Why?
(3) What does Law 8 define? What does Law 9 define?
EXERCISES FOR SECTION 5.5
A Exercises
1. Rewrite the above laws specifying as in Example 5.5.1 the orders of the matrices.
2. Verify each of the Laws of Matrix Algebra using examples.
3. Let A = K
1 2
3 7 6
0 2 4
O, B = K
O, and C = K
O. Compute the following as efficiently as possible by using any of the Laws of
0 1
2 1 5
7 1 1
Matrix Algebra:
(a) A B + A C
(b) A1
(c) A HB + CL
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
(d) HA2 L1
(e) HC + BL1 A1
4. Let A = K
(a) A B
7 4
3 5
O and B = K
O. Compute the following as efficiently as possible by using any of the Laws of Matrix Algebra:
2 1
2 4
(b) A + B
(c) A2 + A B + B A + B 2
(d) B1 A1
(e) A2 + A B
5. Let A and B be n ? n matrices of real numbers. Is A2  B2 = HA  BL HA + BL ? Explain
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
5.6 Matrix Oddities
We have seen that matrix algebra is similar in many ways to elementary algebra. Indeed, if we want to solve the matrix equation A X = B for
the unknown X, we imitate the procedure used in elementary algebra for solving the equation a x = b. Notice how exactly the same properties
are used in the following detailed solutions of both equations.
Solution of a x = b
ax = b
a1 Ha xL = a1 b
Ha1 aL x = a1 b
Solution of A X = B
AX = B
if a ?? 0
A1 HA XL = A1 B
if A1 exists
associative law
HA1 AL X = A1 B
1 x = a1 b
definition of inverse
I X = A1 B
definition of inverse
x = a1 b
identity property of 1
X = A1 B
identity property of I
associative law
Certainly the solution process for A X = B is the same as that of a x = b.
b
The solution of x a = b is x = b a1 = a1 b. In fact, we usually write the solution of both equations as x = . In matrix algebra, the solution of
a
X A = B is X = B A1 , which is not necessarily equal to A1 B. So in matrix algebra, since the commutative law (under multiplication) is not
true, we have to be more careful in the methods we use to solve equations.
B
It is clear from the above that if we wrote the solution of A X = B as X = , we would not know how to interpret the answer
A1 B or B A1 ? Because of this, A1 is never written as
1
A
.
A
B
A
. Does it mean
Some of the main dissimilarities between matrix algebra and elementary algebra are that in matrix algebra:
(1) A B may be different from B A.
(2) There exist matrices A and B such that A B = 0, and yet A ?? 0 and B ?? 0.
(3) There exist matrices A where A ?? 0, and yet A2 = 0.
(4) There exist matrices A where A2  A with A ?? I and A ?? 0
(5) There exist matrices A where A2 = I , where A ?? I and A ?? I
EXERCISES FOR SECTION 5.6
A Exercises
1. Discuss each of the above "oddities" with respect to elementary algebra.
2. Determine 2 ? 2 matrices which show each of the above "oddities" are true.
B Exercises
3. Prove the following implications, if possible:
(a) A2 = A and det A ?? 0 A = I
(b) A2 = I and det A ?? 0 A = I or A = I.
4. Let Mn?n HRL be the set of real n ? n matrices. Let P ? Mn?n HRL be the subset of matrices defined by A ? P if and only if A2 = A. Let
Q ? P be defined by A ? Q if and only if det A ?? 0.
(a) Determine the cardinality of Q.
(b) Consider the special case n = 2 and prove that a sufficient condition for A ? P ? M2?2 HRL is that A has a zero determinant (i.e., A is
singular) and tr HAL = 1 where tr HAL = a11 + a 22 is the sum of the main diagonal elements of A.
(c) Is the condition of part b a necessary condition?
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Chapter 5  Matrix Algebra
C Exercises
5. Write each of the following systems in the form A X = B, and then solve the systems using matrices.
(a) 2 x1 + x2 = 3
x1  x2 = 1
(b) 2 x1  x2 = 4
x1  x2 = 0
(c) 2 x1 + x2 = 1
x1  x2 = 1
(d) 2 x1 + x2 = 1
x1  x2 = 1
(e) 3 x1 + 2 x2 = 1
6 x1 + 4 x2 = 1
6. Recall that p HxL = x2  5 x + 6 is called a polynomial, or more specifically, a polynomial over R, where the coefficients are elements of R
and x ? R. Also, think of the method of solving, and solutions of, x2  5 x + 6 = 0. We would like to define the analogous situation for 2 ? 2
matrices. First define where A is a 2 ? 2 matrix p HAL = A2  5 A + 6 I. Discuss the method of solving and the solutions of
A2  5 A + 6 I = 0.
7. (For those who know calculus)
(a) Write the series expansion for ?a centered around a = 0.
(b) Use the idea of exercise 6 to write what would be a plausible definion of ?A where A is an n ? n matrix.
(c) If A = K
1 1
0 1
? ?1
O and B = K
O , use the series in part (b) to show that ?A = K
O
0 0
0 0
0
1
and ?B = K
1 1
O.
0 1
(d) Show that ?A ?B ?? ?B ?A
(e) Show that ?A+B = K
? 0
O
0 1
(f) Is ?A ?B = ?A+B ?
Applied Discrete Structures by Alan Doerr & Kenneth Levasseur is licensed under a Creative Commons AttributionNoncommercialShareAlike 3.0 United States License.
Paper#9210472  Written in 27Jul2016
Price : $17.85