9) Quadraric Forms Orthogonal And Symmetric Matrices Flashcards
INNER PRODUCT
Properties
The inner product on R^n is the map: The dot product R^n X R^n -> R, (x,y) -> x•y =(x,y) = SUM( x_i y_i) = x^T y Sum from I=1 to n.
Properties: 1) symmetry: x•y = y•x
2) bilinearity: for all x,y,x’,y’ in R^n and for all s,t in R: (sx + tx’) •y = sx•y + tx’•y and x•(sy+ty’) = sx •y + tx •y’
3) non negativity: x•x is always bigger than or equal to 0
Length of vector x
|x| = SQRT( x•x) as always bigger than or equal to 0
x=0 if and only if the length of x is 0
So if length is 0 it’s the 0 vector
If x is not the 0 vector then x/ |x| has length 1 (UNIT VECTOR)
Orthogonality for vectors if x,y in R^n are orthogonal
x,y in R^n are orthogonal if x•y =0
E.g. If inner product =0
If x^T y =0
Orthonormal
A set if vectors v_1,v_2,…,v_n in R^n is called orthonormal if:
v_i • v_j = kronecker delta function = { 1 if I=j, 0 if i≉j}
Eh same vectors dot to give 1 -> length is 1
All others dot gives 0 -> orthogonal
Orthogonal set all with length 1
Theorem for mutually orthogonal vectors
ANY SET OF: n mutually orthogonal Non-zero vectors v_1,...v_n in R^n Form a BASIS FOR R^n.
As they are linearly independent: shown by dot of each to give scalars must be 0. I.e. Taking inner product gives kronecker delta function.
Orthogonal Matrices definition
A nXn matrix is orthogonal if A subset of M_n(R) (Real square Matrices):
Ax•Ay = x•y for all x and y in R^n.
(Ax)^T Ay = x^T A^T Ay =x^Ty
Hence ANY ORTHOGONAL matrix A has an inverse A-1 = A^T
A ∈O(n) if and only if it’s columns, as a set of vectors form an orthonormal set
Orthogonal Matrices properties
The identity matrix E is an orthogonal matrix.
•if A and B are orthogonal Matrices then so is AB
- if A is an orthogonal matrix so is A^T = A^-1
- if A is an orthogonal matrix, then |A| + +1 or -1
Converse not true: there are plenty of Matrices with det A plus or minus 1 that aren’t orthogonal but given that it is orthogonal we know the determinant is either 1 or -1
Orthogonal Matrices
• As a set
The set of all orthogonal nXn Matrices is a group O(n) = {A subset M_n(R) | A^T A =E}
= {A in M_n(R) | A^-1 = A^T }
• A ∈O(n) if and only if it’s columns, as a set of vectors form an orthonormal set by seeing that elements in matrix A^TA are identity matrix elements relating to kronecker delta function
Examples of orthogonal Matrices
Orthogonal Matrices det 1 or -1 but converse not true.
Eh rotation matrix = (cosx, -sinx, sino, cosx)
And permutation Matrices
Symmetric Matrices
A matrix A⊂M_n(R) or a linear map A: R^n -> R^n, x-> Ax
Is called symmetric if
Ax•y = x•Ay for all x,y in R^n
A MATRIX A IS SYMMETRIC IF AND ONLY IF A^T = A
Shown as applying symmetry to inner product and equating
Theorem for symmetric matrix and eigenvalues and eigenvectors
Let A⊂M_n(R) be a SYMMETRIC MATRIX. Then:
1) all eigenvalues of A are REAL numbers
2) for vectors u and v. Au = tu and Av =sv and s≉t then
u•v =0
I.e. For eigenvectors of A with DISTINCT eigenvalues then they are orthogonal with inner product 0
Symmetric matrix and basis
If A is an nXn symmetric matrix then there is an ORTHONORMAL basis for R^n consisting of eigenvectors for A.
BASIS FOR R^n
If NORMALISED THEY DO!!!
As if for n distinct eigenvalues for each eigenvector corresponding length one can be found. Then we have that the set is orthonormal as every dot gives required kronecker delta function.
Since characteristic equation for A is polynomial degree n even eigenvectors form a basis for R^n when eigenvalues not all distinct.
Corollary of symmetric matrices and diagonal
If A is a symmetric nXn matrix then there is an orthogonal matrix P in O(n) and nXn diagonal matrix D st
A = PDP^-1 = PDP^T
Of NORMALISED EIGENVECTORS
For coordinates on V the orthonormal basis reduces to dimensions
Quadratic forms
A quadratic form on R^n is a function of the form
Q(x) = ΣΣq_ij x_i y_j sums from i=1, j=1 to ∞
Set of n^2 constants q_ij in R.
Since we have x_ij = x_ji this implies that b_ij ={ q_ij if I=j, or mean of q_ij and q_ji if i not equal to j)
That is in matrix form coefficients of x_ix_j and x_jx_i are equal.
Symmetrically quadratic form is Q(x) = ΣΣb_ij x_i y_j Where b_ij = b_ji
Defined as a matrix:
Q(x) = x^T B x = (x_1,…,x_n) B Col_vector( x_1,…,x_n)
B is symmetric matrix due to coefficients b_ij.
Example of quadratic form
• Q(x) = x_1 ^2 + 4 x_1 x_2 -3x_2 ^2 is a quadratic form in R^2 with coefficients: q_11= 1, q_12= 4, q_21=0, q_22= -3. Written in symmetric form:
B=R1 [1,2]
R2[2,-3]
Halving the sum of q_12 and q_21