
System of Linear Equation Solutions
Every system of linear equations has either NO solutions, 1 solution, or an INFINITE number of solutions

Consistent
A system of equations (linear or not  over any space) is CONSISTENT if it is a NONEMPTY set

Inconsistent
A system of equations (linear or not  over any space) is INCONSISTENT if it is an EMPTY set

Methods for solving systems of equations
1. Algebraically eliminate variables
2. Simultaneous elimination
3. Gaussian elimination using matrices

RowEchelon Form (13)
Reduced RowEchelon Form (14)
1. First NONzero entry of 1st row is a 1 (leading 1)
 2. Any rows consisting entirely of zeros are grouped together at the bottom on matrix
 3. First NON zero entry of 2nd row is a 1 (occuring farther to the right than leading 1 in higher row
*4. Each column containing a leading 1 has zeros everyhwere else

Homogeneous
A system of linear equations is said to be homogeneous if the constant terms are all zero; the system has the form:

Trivial Solution
Every HOMOGENEOUS system of linear equations is CONSISTENT (since all such systems have 0 as a solution. This is called the TRIVIAL solution

NONtrivial soluton
If there are any other solutions of a system of linear equations other than the trivial solutions, these are called NONtrivial solutions

Theorem:
 Homogeneous Systems
A homogeneous system of linear equations with more unknowns than equations has infinitely many solutions

Definition:
 Matrix
 Entries
A matrix is a rectangualr array of numbers
The numbers in the array are called entries

Column Matrix aka Column Vector
Row Matrix aka Row Vector
A column matrix is a matrix with only one column
A row matrix is a matrix with only one row

Definition:
 Equal Matrices
Two matrices are defined to be equal if they have the same size and their corresponding entries are equal

Theorem:
 Properties of a Zero Matrix
A matrix, all of whose entries are zero is called a zero matrix
1. A + 0 = 0 + A = A
2. A  A = 0
3. 0  A = A
4. A0 = 0; 0A = 0

Square matrix
A matrix with n rows and n columns is called a square matrix

Diagonal Matrix
A square matrix in whichn the entries outside the main diagonal are all zero
*tridiagonal matrices are also square matrices

Upper Triangular Matrix
A square matrix a, such that a_{ij} = 0 if i>j

Lower Triangular Matrix
A square matrix such that a_{ij }= 0 if i < j

Definition:
 Matrix Addition
Matrix Addition is the operation of adding two matrices by adding the corresponding entries together.
*matrices of different sizes cannot be added (or subtracted)

Definition:
 Matrix Multiplication
Matrix multiplication is a binary operation that takes a pair of matrices, and produces another matrix. The product c of matrices a and b is defined as:
c_{ik} = a_{ij} * b_{jk
}*the dimensions of the matrices must satisfy (mxn)(nxp) = (mxp)

Definition:
 Matrix Transpose
The transpose of the (mxn) matrix a is the (nxm) matrix formed by interchaning the rows and columns such trhat row i becomes column i of the transposed matrix denoted by a^{T}

Definition:
 Matrix Trace
The trace of an nbyn square matrix A  denoted by tr(A)  is defined to be the sum of the elements on the main diagonal (the diagonal from the upper left to the lower right) of A

Theorem:
Properties of Matrix Arithmetic
1. A + B = B + A (commutative law of addition)
2. A + (B + C) = (A + B) + C (associative law of addition)
3. A(BC) = AB + AC (associative law of multiplication)
4. A(B + C) = AB + CA / A(B  C) = AB  CA(left distributive law)
5. (B + C)A = BA + CA / (B  C)A = BA  CA(right distributive law)

Theorem:
 Identity Matrices
If R is the rref of an nxn matrix A, then either R has a row of zeros or R is the identity matrix I
*Definiton  An identity matrix has the property that if A is a square matrix, then IA = AI = A
I_{nxn} = [a_{ij}] where a_{ij} = 1 if i = j, a_{ij} = 0 if i does not equal j

Definition:
 Invertible
 Inverse
 Singular
If A is a square matrix, and if a matrix B of the same size can be found such that AB = BA = I, then A is said to be invertible and B is called an inverse of A denoted by A^{1}.
If no such matrix B can be found, then A is said to be singular (not invertible).

Theorem:
Properties of Inverses
If B and C are both inverses of the matrix A, then B = C.
 Proof:
 Since B is an inverse of A, we have BA = I.
 Multiplying both sides on the right by C gives (BA)C = IC = C.
 But (BA)C = B(AC) = BI = B, so that C =B

incomplete Theorem:
 The matrix
 A = [ a b
 c d ]
 is invertible if ad  bc

Theorem:
 Invertible Matrices
 if A and B are invertible martices of the same size, then AB is invertible and
 (AB)^{1} = B^{1} A^{1 }
 Proof:
 If we can show that (AB)(B^{1}A^{1}) = (B^{1}A^{1})(AB) = I,
 then we will have simultaneously shown that the matrix AB is invertible and
 that (AB)^{1} = B^{1} A^{1}.
 But (AB)(B^{1}A^{1}) = A(BB^{1})A^{1} = AIA^{1} = AA^{1} =I.
*A similar argument shows that (B ^{1}A ^{1})(AB) = I

Product of any # of Invertible Matrices
A product of any number of invertible matrices is invertible, and the inverse of the product is the product of the inverses in the reverse order.

