Linear Algebra Worksheet: Vectors and Matrices, Assignments of Differential Equations

This worksheet covers the basics of linear algebra, including vectors, their length, matrices, transposes, conjugates, adjoints, and arithmetic operations such as addition, subtraction, and multiplication. Students will learn how to calculate the dot product of vectors and the product of matrices.

Typology: Assignments

Pre 2010

Uploaded on 08/16/2009

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Math 212: Linear Algebra Name:
The goal of this worksheet is to familiarize your with some basic ideas from linear algebra. By the
end of the worksheet you should feel comfortable working with the following notions:
vectors, the length of vectors and how to mutiply them
a matrix, its transpose, conjugate and adjoint
adding and multiplying matrices
the zero matrix and the identity matrix
(1) Matrices
An m×nmatrix is a rectangular array of numbers (real or complex) with mrows and n
columns.
A=
a11 a12 · · · a1n
a21 a22 · · · a2n
.
.
..
.
..
.
.
am1am2· · · amn
This is sometimes written as A= (aij) for short. We will be concerned mostly about
square, that is n×n, matrices and vectors, that is n×1 matrices. Two matrices are equal
if all of the entries are equal.
(a) Write down your favorite 3 ×2 matrix that contains at least two complex entries and
call it B.
(b) The transpose of a matrix A= (aij )=(aji) = AT. All of the entries get reflected
across the diagonal line from the top left corner to the bottom right corner. So what
was the a24 entry now becomes the a42 entry. Write down BT, where Bis the matrix
from the previous part.
(c) We can also find the complex conjugate of a matrix by conjugating each entry. ¯
A=
aij). Write down ¯
B.
(d) Conjugating and transposing a matrix gives the adjoint, A=¯
AT. Calculate B.
(2) Arithmetic with matrices We can add, subtract and multiply matrices as long as the
sizes of the matrices match up in appropriate ways.
(a) If αis a number (real or complex) then we can multiply a matrix A(aij) by αby
multiplying each entry by α, that is αA= (αaij ). Pick a complex number, call it β,
and calculate βB.
(b) If we have two matrices of the same size then we can add them entry by entry to get
a new matrix. So for A= (aij ) and C= (cij ), the matrix A+C= (aij +cij ). Write
down your next favorite matrix of the appropriate size, call it D, and add it to B.
(c) To subtract Cfrom Awe take (1)Cand then add it to A. So AC=A+ (1)C.
Calculate BD.
(d) The zero matrix is the matrix with all entries equal to 0 and is denoted 0. What is
B+0?
(e) Multiplying matrices is a bit more complicated. If we want to multiply Aby Cto
get the product AC then Amust have then same number columns as Chas rows. In
practice it is clear if it is possible to multiply to matrices or not. The product AC =E
1
pf3

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Math 212: Linear Algebra Name:

The goal of this worksheet is to familiarize your with some basic ideas from linear algebra. By the end of the worksheet you should feel comfortable working with the following notions:

  • vectors, the length of vectors and how to mutiply them
  • a matrix, its transpose, conjugate and adjoint
  • adding and multiplying matrices
  • the zero matrix and the identity matrix

(1) Matrices An m × n matrix is a rectangular array of numbers (real or complex) with m rows and n columns.

A =

a 11 a 12 · · · a 1 n a 21 a 22 · · · a 2 n .. .

am 1 am 2 · · · amn

This is sometimes written as A = (aij ) for short. We will be concerned mostly about square, that is n × n, matrices and vectors, that is n × 1 matrices. Two matrices are equal if all of the entries are equal. (a) Write down your favorite 3 × 2 matrix that contains at least two complex entries and call it B. (b) The transpose of a matrix A = (aij ) = (aji) = AT^. All of the entries get reflected across the diagonal line from the top left corner to the bottom right corner. So what was the a 24 entry now becomes the a 42 entry. Write down BT^ , where B is the matrix from the previous part. (c) We can also find the complex conjugate of a matrix by conjugating each entry. A¯ = (¯aij ). Write down B¯. (d) Conjugating and transposing a matrix gives the adjoint, A∗^ = A¯T^. Calculate B∗.

(2) Arithmetic with matrices We can add, subtract and multiply matrices as long as the sizes of the matrices match up in appropriate ways. (a) If α is a number (real or complex) then we can multiply a matrix A(aij ) by α by multiplying each entry by α, that is αA = (αaij ). Pick a complex number, call it β, and calculate βB. (b) If we have two matrices of the same size then we can add them entry by entry to get a new matrix. So for A = (aij ) and C = (cij ), the matrix A + C = (aij + cij ). Write down your next favorite matrix of the appropriate size, call it D, and add it to B. (c) To subtract C from A we take (−1)C and then add it to A. So A − C = A + (−1)C. Calculate B − D. (d) The zero matrix is the matrix with all entries equal to 0 and is denoted 0. What is B + 0? (e) Multiplying matrices is a bit more complicated. If we want to multiply A by C to get the product AC then A must have then same number columns as C has rows. In practice it is clear if it is possible to multiply to matrices or not. The product AC = E 1

(^2) where E = (e ij ) and

eij =

∑^ n

k=

aikckj.

Let’s do an example.

AC =

1 i − 2 0 −i 4

2 i − 1 1 0 −i 3

1(2i) + i(0) 1(−1) + i(−i) 1(1) + i(3) −2(2i) + 0(0) −2(−1) + 0(−i) −2(1) + 0(3) −i(2i) + 4(0) −i(−1) + 4(−i) −i(1) + 4(3)

2 i 0 1 + 3i − 4 i 2 − 2 2 − 3 i 12 − i

What are we doing here? The entry e 11 is the first row of A is transposed (or “turned vertical”) and dotted with the first column of C. The entry e 12 is the first column of A transposed and then dotted with the second column of C. So the i in eij matches the row of A and the j matches the column of C being dotted. Multiplying matrices works particularly well for square matrices because the product of two n × n matrices is another n × n matrix. Calculate the product

AC =

(f) Now calculate CA. Is AC equal to CA? (g) The identity matrix of size n is denoted I and is the square n × n matrix with 0’s everywhere except along the diagonal where there are 1’s. Calculate

AI =

(h) Yes, this is always true: AI = A = IA. (i) The inverse of a matrix A is denoted A−^1 and is the matrix that satisfies the equation AA−^1 = I = A−^1 A. More about this on Friday.

(3) Vectors

In this course we will write vectors as a column,

x =

x 1 x 2 .. . xn

. So we think of a vector as an n × 1 matrix. The transpose of a vector is a 1 × n matrix. xT^ =

x 1 x 2 · · · xn

. When we talk about multiplying vectors we mean taking the dot product of two vectors. x · y = x 1 y 1 + x 2 y 2 +... + xnyn. Another way to understand the dot product is as the product of xT^ and y as matrices.

xT^ y = x 1 y 1 + x 2 y 2 +... + xnyn = x · y (a) Even though matrix multiplication is not commutative, the dot product is.

Let x =

 (^) and y =

. Show that xT^ y = yT^ x.