User Tools

Site Tools


math121a-f23:october_13_friday

October 13, Friday

Parseval Equality says, Fourier transformation, as a linear map from one function space (function on x), to another function space (function on p), preserves 'norm'. Norm is just a fancy way of saying 'length of a vector'.

What do we mean by the length of a function?

FT Conventions

Continuous Fourier transformation (OK, I switched to Boas convention) $$ f(x) = \int_\R F(p) e^{ipx} dp. $$ $$ F(p) = (1/2\pi) \int_\R f(x) e^{-ipx} dx. $$

Discrete Fourier transformation

Fix a positive integer $N$. $x,p$ are valued in the 'discretized circle' $$ \Z / N\Z \cong \{0,1,\cdots, N-1\}.$$

$$ f(x) = \sum_{p \in \Z / N\Z} F(p) e^{2\pi i \cdot px/N}. $$ $$ F(p) = (1/N) \sum_{x \in \Z / N\Z} f(x) e^{-2\pi i \cdot px/N}. $$

Norm in the Continous Fourier transformation

Let $f(x)$ be a complex valued function on $x \in \R$, we define $$ \| f\|_x^2 := (1/2\pi) \int_\R |f(x)|^2 dx $$

Let $F(p)$ be a complex valued function on $p \in \R$, we define $$ \| F\|_p^2 := \int_\R |F(p)|^2 dp $$

Norm in the Discrete Fourier transformation

$$ \| f\|_x^2 := (1/N) \sum_{x=0}^{N-1} |f(x)|^2 $$

Let $F(p)$ be a complex valued function on $p \in \R$, we define $$ \| F\|_p^2 := \sum_{p=0}^{N-1} |F(p)|^2 $$

Parseval Equality

If $F(p)$ is the Fourier transformation of $f(x)$, then $\|F\|^2_p = \|f\|^2_x. $ We proved in class the discrete case. The continuous case is similar in spirit, but harder to prove.

Convolution

Consider two people, call them Alice and Bob, they each say an integer number, call it a and b. Suppose $a$ and $b$ both have equal probability of taking value within $\{1,2,\cdots, 6\}$, we can ask what is the probabity distribution of $a+b$?

We know $P(a=i) = 1/6$, $P(b=i) = 1/6$ for any $i=1,\cdots, 6$, otherwise the probabilit is 0. Then $$ P(a+b = k) = \sum_{i+j=k} P(a=i) P(b=j). $$

This is an instance of convolution.

convlution in $x$ space

Convolution is usually denoted as $\star$.

If $f$ and $g$ are functions on the $x$ space, then we define $$ (f \star g)(x) = \int_{x_1} f(x_1) g(x-x_1) dx_1 $$ If $F$ and $G$ are functions on the $p$ space, then we define $$ (F \star G)(p) = \int_{p_1} F(p_1) G(p-p_1) dp_1 $$

Fourier transformation sends convolution of functions on one side to simply multiplication on the other side. $$ (1/2\pi) FT(f \star g) = F \cdot G. $$ $$ FT(f \cdot g) = F \star G. $$

math121a-f23/october_13_friday.txt · Last modified: 2023/10/14 01:13 by pzhou