 # Lecture 10 Dimensions, Independence, Basis and Complete Solution of Linear Systems Shang-Hua Teng.

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Lecture 10 Dimensions, Independence, Basis and Complete Solution of Linear Systems
Shang-Hua Teng

Linear Independence Linear Combination Linear Independence
is linearly independent if only if none of them can be expressed as a linear combination of the others

Examples

Linear Independence and Null Space
Theorem/Definition is linearly independent if and only a1v1+a2v2+…+anvn=0 only happens when all a ’s are zero The columns of a matrix A are linearly independent when only solution to Ax=0 is x = 0

2D and 3D v w u v

How do we determine a set of vectors are independent?
Make them the columns of a matrix Elimination Computing their null space

Permute Rows and Continuing Elimination (permute columns)

There must be free variables.
Theorem If Ax = 0 has more have more unknown than equations (m > n: more columns than rows), then it has nonzero solutions. There must be free variables.

Echelon Matrices Free variables

Reduced Row Echelon Matrix R
Free variables

Computing the Reduced Row Echelon Matrix
Elimination to Echelon Matrix E1PA = U Divide the row of pivots by the pivots Upward Elimination E2E1PA = R

Example: Gauss-Jordan Method for Matrix Inverse
[A I] E1[A I] = [U, I] In its reduced Echelon Matrix A-1 [A I] = [I A-1]

A Close Look at Reduced Echelon Matrix
The last equation of R x = 0 is redundant 0 = 0 Rank of A is the number of pivots rank(A).

What is the Rank of Outer Product

Rank and Reduced Row Echelon Matrix
Free variables Theorem/Definition Rank(A) = number of independent rows Rank(A) = number of independent columns

Dimension of the Column Space and Null Space
The dimensions of the column space of A is equal to Rank(A). The dimension of the null space of A is equal to the number of free variables which is n – Rank(A) A is an m by n matrix

Rank and Reduced Row Echelon Matrix
The Pivot columns are not combinations of earlier columns Pivot columns Free variables Free Columns

Reduced Echelon and Null Space Matrix
Special Solutions

Null Space Matrix Ax=0 has n-Rank(A) free variables and special solutions The Nullspace matrix has n-Rank(A) columns The columns of the nullspace matrix are independent The dimension of the Null space is n – rank(A)

Complete Solution of Ax = 0
After column permutation, we can write r pivot columns n-r free columns Nullspace matrix Pivot variables Free variables Moreover: RN = 

Complete Solution to Ax = b
A is an m by n matrix, and b is an n-place vector Unique solution Infinitely many solution No solution Suppose Ax = b has more then one solution, say x1, x2 then A x1 = b A x2 = b So A (x1 - x2 ) = 0 (x1 - x2 ) is in nullspace(A)

Complete Solution to Ax = b
Suppose we found a particular solution xp to Ax = b i.e, A xp = b Let F be the indexes of free variables of Ax = 0 Let xF be the column vector of free variables Let N be the nullspace matrix of A Then defines the complete set of solutions to Ax = b xp

Example: Complete Solution to Ax = b

Augmented matrix [A b] Elimination to obtain [R d]

Set free variables to 0 to find a particular solution
Compute the nullspace matrix Complete solution is

Full Rank Matrix Suppose A is an m by n matrix. Then
A is full column if rank(A) = n columns of A are independent A is full row rank if rank(A) = m Rows of A are independent

Full Column Rank Matrix
Columns are independent All columns of A are pivot columns There are non free variables or special solutions The nullspace N(A) contains only the zero vector If Ax=b has a solution (it might not) then it has only one solution n by n m-n rows of zeros

Full Row Rank Matrix Rows are independent
All rows of A have pivots, R has no zero rows Ax=b has a solution for every right hand side b The column space is the whole space Rm There are n-m special solutions in the null space of A

The Whole Picture Rank(A) = m = n Ax=b has unique solution
Ax=b has n-m dimensional solution Rank(A) = n < m Ax=b has 0 or 1 solution Rank(A) < n, Rank(A) < m Ax=b has 0 or n-rank(A) dimensions

Basis and Dimension of a Vector Space
A basis for a vector space is a sequence of vectors that The vectors are linearly independent The vectors span the space: every vector in the vector can be expressed as a linear combination of these vectors

Basis for 2D and n-D (1,0), (0,1) (1 1), (-1 –2)
The vectors v1,v2,…vn are basis for Rn if and only if they are columns of an n by n invertible matrix

Column and Row Subspace
C(A): the space spanned by columns of A Subspace in m dimensions The pivot columns of A are a basis for its column space Row space: the space spanned by rows of A Subspace in n dimensions The row space of A is the same as the column space of AT, C(AT) The pivot rows of A are a basis for its row space The pivot rows of its Echolon matrix R are a basis for its row space

Important Property I: Uniqueness of Combination
The vectors v1,v2,…vn are basis for a vector space V, then for every vector v in V, there is a unique way to write v as a combination of v1,v2,…vn . v = a1 v1+ a2 v2+…+ an vn v = b1 v1+ b2 v2+…+ bn vn So: 0=(a1 - b1) v1 + (a2 -b2 )v2+…+ (an -bn )vn

Important Property II: Dimension and Size of Basis
If a vector space V has two set of bases v1,v2,…vm . V = [v1,v2,…vm ] w1,w2,…wn . W= [w1,w2,…wn ]. then m = n Proof: assume n > m, write W = VA A is m by n, so Ax = 0 has a non-zero solution So VAx = 0 and Wx = 0 The dimension of a vector space is the number of vectors in every basis Dimension of a vector space is well defined

Dimensions of the Four Subspaces Fundamental Theorem of Linear Algebra, Part I
Row space: C(AT) – dimension = rank(A) Column space: C(A)– dimension = rank(A) Nullspace: N(A) – dimension = n-rank(A) Left Nullspace: N(AT) – dimension = m –rank(A)

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