1. Matrices, Vectors, Determinants.
Ch. 7 Linear Algebra :
Matrices, Vectors, Determinants.
Linear Systems
Systems of linear equations arise in electrical networks, mechanical frameworks, economic models, optimization problems, numeric for differential equation.
Linear algebra uses matrices and vectors as main tools.

2. 7.1 Matrices, Vectors: Addition and Scalar Multiplication
Matrix : A rectangular array of numbers (or functions) enclosed in brackets
Entry (element) : numbers ( or functions ) in the matrix
Row : Horizontal lines of entries
Column : Vertical lines
General Concepts and Notations
Matrices are denoted by capital boldface letters or by writing the general entry in brackets.
An matrix : A matrix with m rows and n columns
Each entry has two subscripts. The first is the row number and the second is the column number.
7.1 Matrices, Vectors: Addition and Scalar Multiplication

3. Square Matrix : An matrix
7.1 Matrices, Vectors: Addition and Scalar Multiplication
Square Matrix : An matrix
Main diagonal : Diagonal containing the entries
Vectors : A matrix with only one row or column
Components : Entries of vector
Vectors are denoted by lowercase boldface letters or by its general component in brackets.
Row Vector : Matrix having just one row
Column Vector : Matrix having just one column

4. Matrix Addition and Scalar Multiplication
7.1 Matrices, Vectors: Addition and Scalar Multiplication
Matrix Addition and Scalar Multiplication
Rules for Matrix Addition and Scalar Multiplication.
Definition Scalar Multiplication (Multiplication by a Number)
: The product of any matrix and any scalar is obtained by multiplying each entry of the
matrix by the scalar.
Definition Addition of Matrices
: The entries in the sum of two matrices are obtained by adding the corresponding entries
Matrices of different sizes cannot be added.
Definition Equality of Matrices
Two matrices have the same size and the corresponding entries are equal.

5. 7.2 Matrix Multiplication
Definition Multiplication of a Matrix by a Matrix
The product C=AB of an matrix times an matrix
r = n
the m matrix with entries
Matrix multiplication is not commutative, in general
Rules for Matrix multiplication.

6. Definition Transposition of Matrices and Vectors
7.2 Matrix Multiplication
Definition Transposition of Matrices and Vectors
Matrix obtained by exchanging rows and columns
Rules for transposition

7. Symmetric Matrix : Matrix whose transpose equals the matrix itself
7.2 Matrix Multiplication
Special Matrices
Symmetric Matrix : Matrix whose transpose equals the matrix itself
Skew- symmetric Matrix : Matrix whose transpose equals minus the matrix
Triangular Matrix
: A square matrix where the entries either below or above the main diagonal are zero.
Upper Triangular Matrix : A square matrices that can have nonzero entries only on and above the
main diagonal, whereas any entry below the diagonal must be zero.
Lower Triangular Matrix : A square matrices that have nonzero entries only on and below the
main diagonal.
Diagonal Matrix : A square matrix in which the entries outside the main diagonal are all zero
Scalar Matrix : A diagonal matrix whose diagonal elements all contain the same scalar.
Unit Matrix ( Identity Matrix) : A scalar matrix whose entries on the main diagonal are all 1.

8. 7.3 Linear Systems of Equations. Gauss Elimination
Linear System, Coefficient Matrix, Augmented Matrix
A linear system of m equations in n unknowns :
Matrix Form of the Linear System : Ax=b
Homogeneous System : all the are zero
Nonhomogeneous System : at least one is not zero
Coefficient Matrix
Solution Vector
Augmented matrix

9. Gauss Elimination and Back Substitution
7.3 Linear Systems of Equations. Gauss Elimination
Gauss Elimination and Back Substitution
Step 1 Elimination of : Add 2 times the first equation to the second equation.
Step 2 Back Substitution : Determination of
Working backward from the last to the first equation of this triangular system
Linear system
Augmented matrix

10. Elementary Row Operations. Row-Equivalent Systems
7.3 Linear Systems of Equations. Gauss Elimination
Elementary Row Operations. Row-Equivalent Systems
<Elementary Operations for Equations>
Interchange of two equations
Addition of a constant multiple of one equation to another equation
Multiplication of an equation by a nonzero constant
<Elementary Row Operations for Matrices>
Interchange of two rows
Addition of a constant multiple of one row to another row
Multiplication of a row by a nonzero constant
These operations are for rows, not for columns.
Row-Equivalent
A linear system row-equivalent to a linear system
can be obtained from by row operations.
Theorem 1 Row-Equivalent Systems
Row-equivalent linear systems have the same set of solutions.

11. Gauss Elimination : The Three Possible Cases of Systems
7.3 Linear Systems of Equations. Gauss Elimination
Gauss Elimination : The Three Possible Cases of Systems
With a unique solution (see Example 2)
With infinitely many solutions( Example 3, below)
Without solutions ( inconsistent systems; see Example 4)

12. Step 1 Elimination of times the first equation to the second equation,
7.3 Linear Systems of Equations. Gauss Elimination
Ex.3 Solve the following linear systems of three equations in four unknowns whose augmented matrix is
Step 1 Elimination of times the first equation to the second equation,
times the first equation to the third equation.
Step 2 Elimination of times the second equation to the third equation.
Step 3 Substitution : From the second equation,
From this and the first equation,
Since remain arbitrary, we have infinitely many solution.

13. Ex.4 Gauss Elimination if no Solution Exists
7.3 Linear Systems of Equations. Gauss Elimination
Ex.4 Gauss Elimination if no Solution Exists
Step 1 Elimination of times the first equation to the second equation,
times the first equation to the third equation.
Step 2 Elimination of
The false statement 0 = 12 shows that the system has no solution.

14. Row Echelon Form and Information From It Row Echelon Form
7.3 Linear Systems of Equations. Gauss Elimination
Row Echelon Form and Information From It
Row Echelon Form
All nonzero rows are above any rows of all zeroes
The leading coefficient of a row is always strictly to the right of the leading coefficient of the
row above it
Three possible cases :
Exactly one solution : r = n and , if present, are zero.
Infinitely many solutions : r < n and , if present, are zero.
No solution : r < m and one of the entries is not zero.

15. 7.4 Linear Independence. Rank of a Matrix. Vector Space
Linear Independence and Dependence of Vectors
(1)
Linearly independent : (1) has only the trivial solution.
Linearly dependent : (1) holds with scalars not all zero.

16. Rank : The maximum number of linearly independent row vectors
7.4 Linear Independence. Rank of a Matrix. Vector Space
Rank of a Matrix
Definition
Rank : The maximum number of linearly independent row vectors
Theorem 1 Row-Equivalent Matrix
Row-equivalent matrices have the same rank.
Theorem 2 Linear Independence and Dependence of Vectors
p vectors with n components each are linearly independent if the matrix with these vectors as
row vectors has rank p, but they are linearly dependent if that rank is less than p.
Theorem 3 Rank in Terms of Column Vectors
The rank r equals the maximum number of linearly independent column vectors.
Hence the matrix and its transpose have the same rank.
Theorem 4 Linear Dependence of Vectors
p vectors with n < p components are always linearly dependent.

17. Dimension : The maximum number of linearly independent vectors in V
7.4 Linear Independence. Rank of a Matrix. Vector Space
Vector Space : A set V of vectors such that with any two vectors in V all their linear
combinations are elements of V, and these vectors satisfy the following laws.
Dimension : The maximum number of linearly independent vectors in V
Basis : A linearly independent set in V consisting of a maximum possible number of vectors in V
Span : The set of all linear combinations of given vectors with the same number of components.
Subspace : A nonempty subset that forms itself a vector space with respect to the two algebraic
operations ( addition and scalar multiplication ) defined for the vectors,

18. Theorem 6 Row Space and Column Space
7.4 Linear Independence. Rank of a Matrix. Vector Space
Theorem 5 Vector Space
The vector space consisting of all vectors with n components ( n real numbers ) has
dimension n.
Theorem 6 Row Space and Column Space
The row space and the column space of a matrix A have the same dimension, equal to rank A.
Null Space : The solution set of the homogeneous system Ax = 0
Nullity : The dimension of the null space
rank A + nullity A = Number of columns of A

19. 7.5 Solutions of Linear Systems: Existence, Uniqueness
Theorem 1 Fundamental Theorem for Linear Systems
Existence : A linear system of m equations in n unknowns is consistent, that is, has solutions, if
and only if the coefficient matrix and the augmented matrix have same rank.
Uniqueness : The linear system has precisely one solution if and only if this common rank r of
the coefficient matrix and the augmented matrix equals n.
Infinitely Many Solutions : If this common rank r is less than n, the system has infinitely many
solutions. All of these solutions are obtained by determining r suitable unknowns
(whose submatrix of coefficients must have rank r ) in terms of the remaining n – r
unknowns, to which arbitrary values can be assigned.
Gauss Elimination : If solutions exist, they can all be obtained by the Gauss elimination. ( This
method will automatically reveal whether or not solutions exist)

20. Homogeneous Linear System
7.5 Solutions of Linear Systems: Existence, Uniqueness
Homogeneous Linear System
Theorem 2 Homogeneous Linear System
A homogeneous linear system of m equations in n unknowns always has the trivial solution.
Nontrivial solutions exist if and only if rank A < n.
If rank A = r < n, these solutions, together with x = 0, form a vector space of dimension n – r,
called the solution space.
Linear combination of two solution vectors of the homogeneous linear system is a solution
vector.
Theorem 3 Homogeneous Linear System with Fewer Equations Than Unknowns
A homogeneous linear system with fewer equations than unknowns has always nontrivial
solutions.

21. Nonhomogeneous Linear Systems
Theorem 4 Nonhomogeneous Linear System
If a nonhomogeneous linear system is consistent, then all of its solutions are obtained as
where is any solution of the nonhomogeneous linear system and runs through
all the solutions of the corresponding homogeneous system.

22. 7.6 For Reference : Second- and Third-Order Determinants
Determinant of Second Order :
Cramer’s rule

23. Determinant of Third Order :
7.6 For Reference : Second- and Third-Order Determinants
Determinant of Third Order :
Cramer’s rule

24. 7.7 Determinants. Cramer’s Rule
Determinant of Order n :
If n = 1 then If
The minor of in D : A determinant of order n -1
The cofactor of in D :

25. General Properties of Determinants
7.7 Determinants. Cramer’s Rule
General Properties of Determinants
Theorem 1 Behavior of an nth-Order Determinant under Elementary Row Operations
Interchange of two rows multiplies the value of the determinant by -1.
Addition of a multiple of a row to another row does not alter the value of the determinant.
Multiplication of a row by a nonzero constant c multiplies the value of the determinant by c.
Theorem 2 Further Properties of nth-Order Determinants
Interchange of two columns multiplies the value of the determinant by -1.
Addition of a multiple of a column to another column does not alter the value of the determinant.
Multiplication of a column by a nonzero constant c multiplies the value of the determinant by c.
Transposition leaves the value of a determinant unaltered.
A zero row or column renders the value of a determinant zero.
Proportional rows or columns render the value of a determinant zero.

26. Theorem 4 Cramer’s Rule(Solution of Linear Systems by Determinants)
7.7 Determinants. Cramer’s Rule
Theorem 4 Cramer’s Rule(Solution of Linear Systems by Determinants)
is the determinant obtained from D by replacing in D the kth column by the column
with the entries
Theorem 3 Rank in Terms of Determinants
An matrix has rank if and only if A has an submatrix with
nonzero determinant, whereas every square submatrix with more than r rows that A has
determinant equal to zero.
In particular, if A is square, , it has rank n if and only if
Cramer’s rule
Cramer’s Rule

27. 7.8 Inverse of a Matrix. Gauss-Jordan Elimination
Theorem 1 Existence of the Inverse
The inverse of an matrix A exists if and only if rank A = n, thus if and only if det A 0
A is nonsingular if rank A = n
A is singular if rank A < n
7.8 Inverse of a Matrix. Gauss-Jordan Elimination
Inverse Matrix
Nonsingular Matrix : The matrix whose determinant is not equal to zero
Singular Matrix : A square matrix whose determinant is zero
If the matrix has an inverse, the inverse is unique.

28. Determination of the Inverse by the Gauss-Jordan Method
7.8 Inverse of a Matrix. Gauss-Jordan Elimination
Determination of the Inverse by the Gauss-Jordan Method
Gauss elimination
Ex.1 Determine the inverse of

29. Theorem 2 Inverse of a Matrix
7.8 Inverse of a Matrix. Gauss-Jordan Elimination
Theorem 2 Inverse of a Matrix
Ex.3 Find the inverse of

30. The inverse of the diagonal matrices
7.8 Inverse of a Matrix. Gauss-Jordan Elimination
The inverse of the diagonal matrices
Ex.4 The inverse of is
Products :
The inverse of the inverse :

31. det(AB)=det(BA)=detA detB
7.8 Inverse of a Matrix. Gauss-Jordan Elimination
Unusual Properties of Matrix Multiplication. Cancellation Laws
Matrix multiplication is not commutative, that is, in general we have
AB = 0 does not generally imply A = 0 or B = 0; for example,
AC = AD does not generally imply C = D (even when ).
Theorem 4 Determinant of Product of Matrices
det(AB)=det(BA)=detA detB
Theorem 3 Cancellation Laws
Let A, B, C be matrices. Then :
If rank A = n and AB = AC, then B = C.
If rank A = n, then AB = 0 implies B = 0.
Hence if AB = 0, but as well as , then rank A < n and rank B < n.
If A is singular, so are BA and AB.

32. 7.9 Vector Spaces, Inner Product Spaces, Linear Transformations
Definition Real Vector Space
Vector addition : a + b
Commutativity a + b = b +a
Associativity ( u + v ) + w = u + ( v + w )
Zero Vector a + 0 = a
a + (-a ) = 0
Scalar multiplication : ka
Distributivity c (a + b) = ca + cb
Distributivity (c + k ) a = ca + ka
Associativity c ( ka ) = (ck ) a
1a = a
7.9 Vector Spaces, Inner Product Spaces, Linear Transformations

33. Orthogonal : Vectors whose product is zero Norm :
7.9 Vector Spaces, Inner Product Spaces, Linear Transformations
Orthogonal : Vectors whose product is zero
Norm :
Unit Vector : A vector of norm 1
Basic inequality
Cauchy-Schwarz inequality.
Triangle inequality.
Parallelogram equality.
Definition Real Inner Product Space
Inner Product : ( a , b )
Linearity ( qa + kb , c ) = q ( a , c ) + k ( b , c )
Symmetry ( a , b ) = ( b , a )
Positive-definiteness ,
( a , a ) = 0 if and only if a = 0

34. Linear Transformations
7.9 Vector Spaces, Inner Product Spaces, Linear Transformations
Linear Transformations
A mapping (transformation, operator ) of X into Y
: We assign a unique vector y in Y to each vector x in X
Linear transformation F : for all vectors v and x in X and scalars c,
F ( v + x ) = F ( v ) + F ( x )
F ( cx ) = c F ( x )
Linear Transformation of Space into Space
Any real matrix gives a transformation of space into space
y = Ax
This transformation is linear