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Introduction• Matrices seem to have been
developed by Gauss, for the purpose of solving systems of simulteneous linear equations– Before 1800s they are known as
arrays– A Chinese text discusses their use for
linear equations that dates to BC times
• We will also use but not for that purpose, but lets start with that
Copyright © 2014 Curt Hill
A matrix• When we have systems of
linear equations the first step is to put them in standard form:a1x+b1y=c1
a2x+b2y=c2
• In standard form we always write the equations in the same way
• On the left hand side are all the variable and on the right only constants
• The variables are in the same order
Copyright © 2014 Curt Hill
Consider• Two equations
3x - 7y = 12x - 3y = -1
• This is now in standard form:• All variables to left of =• All constants to right• Xs always come first and Ys next• The first column is the X• The second column is the Y• The third the constant
• The next step is to render this in terms of a matrix
Copyright © 2014 Curt Hill
Matrix Example
• Three columns and two rows• Each column is the coefficients of
some part of the equation• Each row represents an equation
Copyright © 2014 Curt Hill
Gaussian Elimination• To solve a system of simultaneous linear equations you transform the matrix into this form:
• This can be done by the application of two rules only:– Any row may be multiplied by a
constant– The sum or difference of any two rows
may replace any row– The shortcut is to multiply a row by a
constant and add it to another, replacing either
Copyright © 2014 Curt Hill
In General• We may have any number of rows
and columns• A column matrix has one column
but multiple rows– Also known as a column vector
• A row matrix has one row but multiple columns– Also known as a row vector
• A square matrix has the same number of rows and columns
Copyright © 2014 Curt Hill
Elements
• Each element is subscripted by a row number then a column number
• A row or column matrix might only have one subscript
Copyright © 2014 Curt Hill
Arithmetic• We also have the notion of matrix
arithmetic– At least addition, subtraction, product
(multiplication)• In the rationals and real numbers
any two items may participate in any operation
• This is not true for matrix arithmetic– Two matrices must be compatible– The rules of compatibility vary by
operationCopyright © 2014 Curt Hill
Matrix Addition• Two matrices are compatible for
addition if they have the same sizes– The two must have the same number
of columns and same number of rows• The sum of two matrices is a new
matrix with the same size of the two that were added
• Each position has the sum of the corresponding positions of the two
Copyright © 2014 Curt Hill
Addition Again• We write C = A + B• The following statement must then
be true of Ci,j Ci,j = Ai,j + Bi,j
– The subscripts i and j must be in correct bounds for all three
• We may also define subtraction in the same way
Copyright © 2014 Curt Hill
Audience Participation• Consider the two matrices:
and • Are A+B compatible for addition?• What is A+B?
Copyright © 2014 Curt Hill
Multiplication• Multiplication is somewhat more
complicated• There are two things to consider:
– Matrix compatibility– How the operation proceeds
• Neither is as easy as addition• Matrix multiplication is not
commutative• We denote the product of matrix A
and matrix B as ABCopyright © 2014 Curt Hill
Compatibility• The matrix AB may only be
computed if the number of columns of A is identical with the number of rows of B– The number of rows of A and number
of columns of B do not matter• Thus if A is mk and B is kn the
two are compatible• Square matrices of the same size
are compatible, but all other compatible matrices have a different shapeCopyright © 2014 Curt Hill
Process• If A is mk and B is kn the result,
AB is mn• The new matrix has each element
as a sum of products• A column from the left times the row
from right• C=AB
Cij = Ai1B1j + Ai2B2j + … + AikBkj
Copyright © 2014 Curt Hill
Something else• Now lets try one in groups:• If A= • What is AB?• What is BA?
Copyright © 2014 Curt Hill
Identity Matrix• A square matrix with ones on the
diagonal and zeros else where is an identity matrix
• When multiplied with a compatible matrix it gives the matrix back– Similar to one with respect to
multiplication and zero with respect to addition
– One is the unit of multiplication as is zero to addition
Copyright © 2014 Curt Hill
Online Resources• The following links may be helpful:• http://matrix.reshish.com• http://www.wolframalpha.com/• First two on a Google Search
Copyright © 2014 Curt Hill
Transpose• A common operation of matrices• We make rows into columns
– Flip it around the diagonal• Thus the transpose of:
is • A square matrix that does not
change with a transpose is symmetric– Such as the identity matrix
Copyright © 2014 Curt Hill
Inverse Matrix• Two square matrices of same size
are inverses of one another if their product is the identity matrix– They are invertable
• Thus AB = BA = I• An inverse is unique• We also use the notation A-1 for the
inverse of A• In linear algebra a coefficient
matrix that is invertable has a unique solution
Copyright © 2014 Curt Hill
Zero-One Matrix• Any matrix, such as the identity
matrix, that has only zeros and ones
• Often matrices representing Boolean values
• The join of two identically sized zero one matrices is the corresponding elements ORed
• The meetjoin of two identically sized zero one matrices is the corresponding elements ANDedCopyright © 2014 Curt Hill
Example • If we have two zero-one matrices:
• The join is: • The meet is:
Copyright © 2014 Curt Hill
Boolean Product• Similar to matrix multiplication
except the adds are replaced by ORs and multiplies by ANDs
• The symbol is
Copyright © 2014 Curt Hill
Powers• Raising a number to a power is
merely repeated multiplication• We may also raise square zero-one
matrices to a power• Conjunction and disjunction are
associative so Boolean product is as well
• We define the zeroth power to be an identity matrix
Copyright © 2014 Curt Hill