8. Linear Transformations
8.1 Linear Requires
Keys:
A linear transformation T takes vectors v to vectors T(v). Linearity requires:
\[T(cv +dw) = cT(v) + dT(w)
\]The input vectors v and outputs T(v) can be in \(R^n\) or matrix space or function space.
If A is m by n, \(T(x)=Ax\) is linear from the input space \(R^n\) to the output space \(R^m\).
The derivative \(T(f)=\frac{df}{dx}\) is linear.The integral \(T^+(f)=\int^x_0f(t)dt\) is its pseudoinverse.
Derivative: \(1,x,x^2 \rightarrow 1,x\)
\[u = a + bx + cx^2 \\
\Downarrow \\
Au = \left [ \begin{matrix} 0&1&0 \\ 0&0&2 \end{matrix} \right]
\left [ \begin{matrix} a \\ b \\ c \end{matrix} \right]
=\left [ \begin{matrix} b \\ 2c \end{matrix} \right] \\
\Downarrow \\
\frac{du}{dx} = b + 2cx
\]Integration: \(1,x \rightarrow x,x^2\)
\[\int^x_0(D+Ex)dx= Dx + \frac{1}{2}Ex^2 \\
\Downarrow \\
input \ \ v \ \ (D+Ex) \\
A^+v = \left[ \begin{matrix} 0&0 \\ 1&0 \\ 0&\frac{1}{2} \end{matrix} \right]
\left[ \begin{matrix} D \\ E \end{matrix} \right]
=\left[ \begin{matrix} 0 \\D \\ \frac{1}{2}E \end{matrix} \right] \\
\Downarrow \\
T^+(v) = Dx + \frac{1}{2}Ex^2
\]The product ST of two linear transformations is still linear : \((ST)(v)=S(T(v))\)
Linear : rotated or stretched or other linear transformations.
8.2 Matrix instead of Linear Transformation
We can assign a matrix A to instead of every linear transformation T.
For ordinary column vectors, the input v is in \(V=R^n\) and the output \(T(v)\) is in \(W=R^m\), The matrix A for this transformation will be m by n.Our choice of bases in V and W will decide A.
8.2.1 Change of Basis
if \(T(v) = v\) means T is the identiy transformation.
If input bases = output bases, then the matrix \(I\) will be choosed.
If input bases not equal to output bases, then we can construct new matrix \(B=W^{-1}V\).
example:
\[input \ \ basis \ \ [v_1 \ \ v_2] = \left [ \begin{matrix} 3&6 \\ 3&8 \end{matrix} \right] \\
output \ \ basis \ \ [w_1 \ \ w_2] = \left [ \begin{matrix} 3&0 \\ 1&2 \end{matrix} \right] \\
\Downarrow \\
v_1 = 1w_1 + 1w_2 \\
v_2 = 2w_1 + 3w_2 \\
\Downarrow \\
[w_1 \ \ w_2] [B] = [v_1 \ \ v_2] \\
\Downarrow \\
\left [ \begin{matrix} 3&0 \\ 1&2 \end{matrix} \right]
\left [ \begin{matrix} 1&2 \\ 1&3 \end{matrix} \right]
=
\left [ \begin{matrix} 3&6 \\ 3&8 \end{matrix} \right]
\]when the input basis is in the columns of V, and the output basis is in the columns of W, the change of basis matrix for \(T\) is \(B=W^{-1}V\).
Suppose the same vector u is written in input basis of v's and output basis of w's:
\[u=c_1v_1 + \cdots + c_nv_n \\
u=d_1w_1 + \cdots + d_nw_n \\
\left [ \begin{matrix} v_1 \cdots v_n \end{matrix} \right]
\left [ \begin{matrix} c_1 \\ \vdots \\ c_n \end{matrix} \right]
=
\left [ \begin{matrix} w_1 \cdots w_n \end{matrix} \right]
\left [ \begin{matrix} d_1 \\ \vdots \\ d_n \end{matrix} \right]
\\
Vc=Wd \\
d = W^{-1}Vc = Bc \\
\]c is coordinates of input basis, d is coordinates of output basis.
8.2.2 Construction Matrix
Suppose T transforms the space V to space W. We choose a basis \(v_1,v_2,...,v_n\) for V and a basis \(w_1,w_2,...,w_n\) for W.
=a_{1j}w_1 + \cdots + a_{mj}w_m
\]
The \(a_{ij}\) are into A.
T(v) =\frac{dv}{dx} = 1c_2 + 2c_3x + 3c_4x^2 \\
Ac=\left[ \begin{matrix} 0&1&0&0 \\ 0&0&2&0\\ 0&0&0&3 \end{matrix} \right]
\left[ \begin{matrix} c_1 \\ c_2 \\ c_3 \\ c_4 \end{matrix} \right]
=\left[ \begin{matrix} c_2 \\ 2c_3 \\ 3c_4 \end{matrix} \right]
\]
T takes the derivative, A is "derivative matrix".
8.2.3 Choosing the Best Bases
The same T is represented by different matrices when we choose different bases.
Perfect basis
Eigenvectors are the perfect basis vectors.They produce the eigenvalues matrix \(\Lambda = X^{-1}AX\)
Input basis = output basis
The new basis of b's is similar to A in the standard basis:
\]
Different basis
Probably A is not symmetric or even square, we can choose the right singular vectors (\(v_1,...,v_n\)) as input basis and the left singular vectors(\(u_1,...,u_n\)) as output basis.
\]
\(\Sigma\) is "isometric" to A.
Definition : \(C=Q^{-1}_1AQ_{2}\) is isometric to A if \(Q_1\) and \(Q_2\) are orthogonal.
8.2.4 The Search of a Good Basis
Keys: fast and few basis.
- $B_{in} = B_{out} = $ eigenvector matrix X . Then \(X^{-1}AX\)= eigenvalues in \(\Lambda\).
- $B_{in} = V \ , \ B_{out} = U $ : singular vectors of A. Then \(U^{-1}AV\)= singular values in \(\Sigma\).
- $B_{in} = B_{out} = $ generalized eigenvectors of A . Then \(B^{-1}AB\)= Jordan form \(J\).
- $B_{in} = B_{out} = $ Fourier matrix F . Then \(Fx\) is a Discrete Fourier Transform of x.
- The Fourier basis : \(1,sinx,cosx,sin2x,cos2x,...\)
- The Legendre basis : \(1, x, x^2 - \frac{1}{3},x^3 - \frac{3}{5},...\)
- The Chebyshev basis : \(1, x, 2x^2 - 1,4x^3 - 3x,...\)
- The Wavelet basis.
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