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