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# Math4121 Lecture 31
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## Chapter 3: Lebesgue Integration
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### Non-measurable sets
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#### Definition: Vitali's construction
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Step 1. Define an equivalence relation on $\mathbb{R}$ as follows:
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Recall a relation is an equivalence relation if it is reflexive, symmetric, and transitive.
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1. Reflexive: $x\sim x$ for all $x\in\mathbb{R}$
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2. Symmetric: $x\sim y$ implies $y\sim x$ for all $x,y\in\mathbb{R}$
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3. Transitive: $x\sim y$ and $y\sim z$ implies $x\sim z$ for all $x,y,z\in\mathbb{R}$
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Say $x\sim y$ if $x-y\in\mathbb{Q}$.
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This is an equivalence relation, easy to show by the properties above.
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We denote the equivalence class of $x$ by $\mathbb{R}/\sim$, where $[x]=\{x+q:q\in\mathbb{Q}\}$.
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If $z\in [x]$, then so is the fractional part of $z$, i.e. $z-\lfloor z\rfloor\in [x]$. So in every equivalence class $[x]$ we can find an element in $[x]\cap (0,1)$. Take one such real number from every equivalence class, and call the set of all such numbers $\mathcal{N}$.
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Step 2. Show that $\mathcal{N}$ is not Lebesgue measurable.
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We defined the translation of $S$ as follows:
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Given a set $S\subseteq\mathbb{R}$ and a real number $a\in\mathbb{R}$, the translation of $S$ by $a$ is defined as
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$$
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S+a=\{x+a:x\in S\}
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$$
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Outer measure is translation invariant, i.e. $m_e(S+a)=m_e(S)$ for all $S\subseteq\mathbb{R}$ and $a\in\mathbb{R}$, which also holds for inner measure.
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Properties of $\mathcal{N}$:
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1. $(0,1)\subseteq\bigcup_{q\in \mathbb{Q}\cap (-1,1)} (\mathcal{N}+q)\subseteq (-1,2)$
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2. $\{\mathcal{N}+q:q\in\mathbb{Q}\cap (-1,1)\}$ is pairwise disjoint.
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Suppose $\mathcal{N}$ is measurable. Then by (1)
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$$
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\begin{aligned}
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1&\leq \sum_{q\in\mathbb{Q}\cap (-1,1)} (\mathcal{N}+q)\\
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&=\sum_{q\in\mathbb{Q}\cap (-1,1)} m(\mathcal{N})
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\end{aligned}
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$$
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So $m(\mathcal{N})\neq 0$.
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By (2), we have
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$$
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\begin{aligned}
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3&\geq \sum_{q\in\mathbb{Q}\cap (-1,1)} m(\mathcal{N}+q)\\
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&=\sum_{q\in\mathbb{Q}\cap (-1,1)} m(\mathcal{N})\\
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&=m(\mathcal{N})\sum_{q\in\mathbb{Q}\cap (-1,1)} 1\\
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&=\infty
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\end{aligned}
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$$
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This is a contradiction. So $\mathcal{N}$ is not Lebesgue measurable.
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QED
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Appendix:
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(1) $I\subseteq\bigcup_{q\in\mathbb{Q}\cap (-1,1)} (\mathcal{N}+q)$
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Let $x\in I$. We need to find $q\in\mathbb{Q}\cap (-1,1)$ such that $x-q\in\mathcal{N}$. $\exists y\in\mathcal{N}$ such that $y\in (0,1)\cap [x]$. Then $x-y=q\in \mathbb{Q}$ and since $x,y\in I$, we have $q\in (-1,1)$.
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(2) $\{\mathcal{N}+q:q\in\mathbb{Q}\cap (-1,1)\}$ is pairwise disjoint.
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Suppose $\mathcal{N}+q_1=\mathcal{N}+q_2$ for some $q_1,q_2\in\mathbb{Q}\cap (-1,1)$. We want to show $q_1=q_2$.
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Take $x$ in the intersection, then this means $y=x-q_1, z=x-q_2\in\mathcal{N}$.
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But $y\sim z$, this contradicts the fact that $\mathcal{N}$ contains only one element from each equivalence class. So $q_1=q_2$.
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#### Axiom of choice
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Given a set $S$, $\exists \psi:\mathscr{P}(S)\to S$ such that $\psi(T)\in T, \forall T\subseteq\mathscr{P}(S)$.
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For any set $S$, there exists a map that maps every non-empty subset of $S$ to an element of that subset.
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This leads to some weird results, e.g. Banach-Tarski paradox.
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_Godel showed that the axiom of choice is not contradictory to ZF set theory._ You have ZFC
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_Cohen showed that the negation of the axiom of choice is not contradictory to ZF set theory._ You have ZF
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You can choose the axiom or not.
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# Math4121 Lecture 32
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## Chapter 6: The Lebesgue Integral
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### Measurable Functions
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Definition: A function $f:\mathbb{R}\to\mathbb{R}$ is measurable on the interval $[a,b]$ if $\{x\in [a,b]:f(x) > c\}$ is measurable for all $c\in\mathbb{R}$, called the **super level set** of $f$.
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Denote $\{f> c\}$
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#### Proposition 6.1
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The following are equivalent:
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For all $c\in\mathbb{R}$,
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1. $\{x\in [a,b]:f(x) > c\}$ is measurable.
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2. $\{x\in [a,b]:f(x) < c\}$ is measurable.
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3. $\{x\in [a,b]:f(x) \geq c\}$ is measurable.
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4. $\{x\in [a,b]:f(x) \leq c\}$ is measurable.
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5. $\{x\in \mathbb{R}:c \leq f(x) < d\}$ is measurable for all $c,d\in\mathbb{R}$.
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Proof:
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Since the complement of a measurable set is measurable. (1) $\iff$ (4). and (2) $\iff$ (3).
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We only need to show (1) $\implies$ (2).
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Since $\{f>c\}=\bigcup_{n=1}^{\infty}\{f\geq c+\frac{1}{n}\}$.
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So (1) $\implies$ (2).
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Since (2) $\implies$ (1)-(4) $\implies$ (5).
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To see (5) $\implies$ (1), we have $\{f\geq c\}=\bigcup_{n=1}^{\infty}\{x\in\mathbb{R}:c \leq f(x) < c+n\}$
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QED
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#### Proposition 6.3
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Let $f,g$ be measurable on $[a,b]$ and $\alpha\in\mathbb{R}$. Then the following are measurable:
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1. $f+g$
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2. $fg$
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3. $\alpha f$
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4. $|f|^\alpha$
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Proof:
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If $\alpha=0$, then $\alpha f$ and $|f|^\alpha$ are constant functions, hence measurable.
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But for constant functions $h$, $\{h>c\}=\begin{cases}
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\emptyset & \text{if } c\geq h \\
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\mathbb{R} & \text{if } c < h
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\end{cases}$
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For $a\neq 0$, $\{x\in \mathbb{R}:\alpha f(x) > c\}=\{x\in \mathbb{R}:f(x) > \frac{c}{\alpha}\}$
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Similarly, $\{x\in \mathbb{R}:|f(x)|^\alpha > c\}=\{x\in \mathbb{R}:|f(x)| > c^{1/\alpha}\}=\{x\in \mathbb{R}:f(x) > c^{1/\alpha}\}\cup\{x\in \mathbb{R}:f(x) < -c^{1/\alpha}\}$
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We want to show $\{f+g>c\}=\bigcup_{q\in \mathbb{Q}}\{f>q\}\cap\{g>c-q\}$
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$\{f+g>c\}\supseteq \bigcup_{q\in \mathbb{Q}}\{f>q\}\cap\{g>c-q\}$
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if $x$ is in the RHS, then $\exists q\in \mathbb{Q}$ such that $f(x) > q$ and $g(x) > c-q$, therefore $f(x)+g(x) > c$.
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So $x$ is in the LHS.
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$\{f+g>c\}\subseteq \bigcup_{q\in \mathbb{Q}}\{f>q\}\cap\{g>c-q\}$
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Let $x\in \mathbb{R}$ such that $f(x)+g(x) > c$. Need to find $q\in \mathbb{Q}$ such that $f(x) > q$ and $g(x) > c-q$.
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Since $f(x)>c-g(x)$, by the density of $\mathbb{Q}$ in $\mathbb{R}$, $\exists q\in \mathbb{Q}$ such that $q > c-g(x)$.
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For $fg$, we have $fg=\frac{1}{4}((f+g)^2-(f-g)^2)$
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So $fg$ is measurable.
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QED
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### Limit of Measurable Functions
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#### Proposition 6.4
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Let $\{f_n\}$ be a sequence of measurable functions on $[a,b]$. Then,
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$$
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g(x)=\sup_{n\geq 1}f_n(x),\inf_{n\geq 1}f_n(x),\limsup_{n\to\infty}f_n(x),\liminf_{n\to\infty}f_n(x)
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$$
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are all measurable functions
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#### Corollary of Proposition 6.4
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If $\{f_n\}_{n=1}^{\infty}$ are measurable functions and $f(x)=\lim_{n\to\infty}f_n(x)$ exists for all $x\in[a,b]$, then $f$ is measurable. (pointwise limit of measurable functions is measurable)
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#### Definition of almost everywhere
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A property holds **almost everywhere** if it holds everywhere except for a set of measure zero.
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@@ -126,6 +126,8 @@ def is_uniquely_decipherable(f):
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return True
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return True
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```
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```
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### Shannon's source coding theorem
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#### Definition 1.1.4
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#### Definition 1.1.4
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An elementary information source is a pair $(A,\mu)$ where $A$ is an alphabet and $\mu$ is a probability distribution on $A$. $\mu$ is a function $\mu:A\to[0,1]$ such that $\sum_{a\in A}\mu(a)=1$.
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An elementary information source is a pair $(A,\mu)$ where $A$ is an alphabet and $\mu$ is a probability distribution on $A$. $\mu$ is a function $\mu:A\to[0,1]$ such that $\sum_{a\in A}\mu(a)=1$.
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L(\mu)=\min\{\overline{l}(\mu,f)|f:A\to S(B)\text{ is uniquely decipherable}\}
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L(\mu)=\min\{\overline{l}(\mu,f)|f:A\to S(B)\text{ is uniquely decipherable}\}
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$$
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$$
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#### Lemma: Jenson's inequality
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Let $f$ be a convex function on the interval $(a,b)$. Then for any $x_1,x_2,\cdots,x_n\in (a,b)$ and $\lambda_1,\lambda_2,\cdots,\lambda_n\in [0,1]$ such that $\sum_{i=1}^{n}\lambda_i=1$, we have
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$$f(\sum_{i=1}^{n}\lambda_ix_i)\leq \sum_{i=1}^{n}\lambda_if(x_i)$$
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Proof:
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If $f$ is a convex function, there are three properties that useful for the proof:
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1. $f''(x)\geq 0$ for all $x\in (a,b)$
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2. For any $x,y\in (a,b)$, $f(x)\geq f(y)+(x-y)f'(y)$ (Take tangent line at $y$)
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3. For any $x,y\in (a,b)$ and $0<\lambda<1$, we have $f(\lambda x+(1-\lambda)y)\leq \lambda f(x)+(1-\lambda)f(y)$ (Take line connecting $f(x)$ and $f(y)$)
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We use $f(x)\geq f(y)+(x-y)f'(y)$, we replace $y=\sum_{i=1}^{n}\lambda_ix_i$ and $x=x_j$, we have
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$$f(x_j)\geq f(\sum_{i=1}^{n}\lambda_ix_i)+(x_j-\sum_{i=1}^{n}\lambda_ix_i)f'(\sum_{i=1}^{n}\lambda_ix_i)$$
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We sum all the inequalities, we have
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$$
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\begin{aligned}
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\sum_{j=1}^{n}\lambda_j f(x_j)&\geq \sum_{j=1}^{n}\lambda_jf(\sum_{i=1}^{n}\lambda_ix_i)+\sum_{j=1}^{n}\lambda_j(x_j-\sum_{i=1}^{n}\lambda_ix_i)f'(\sum_{i=1}^{n}\lambda_ix_i)\\
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&\geq \sum_{j=1}^{n}\lambda_jf(\sum_{i=1}^{n}\lambda_ix_i)+0\\
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&=f(\sum_{j=1}^{n}\lambda_ix_j)
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\end{aligned}
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$$
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#### Theorem 1.1.5
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#### Theorem 1.1.5
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Shannon's source coding theorem
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Shannon's source coding theorem
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$\log \prod_{a\in A}\left(\frac{v(a)}{\mu(a)}\right)^{\mu(a)}=\sum_{a\in A}\mu(a)\log \frac{v(a)}{\mu(a)}$ is also called Kullback–Leibler divergence or relative entropy.
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$\log \prod_{a\in A}\left(\frac{v(a)}{\mu(a)}\right)^{\mu(a)}=\sum_{a\in A}\mu(a)\log \frac{v(a)}{\mu(a)}$ is also called Kullback–Leibler divergence or relative entropy.
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> Jenson's inequality: Let $f$ be a convex function on the interval $(a,b)$. Then for any $x_1,x_2,\cdots,x_n\in (a,b)$ and $\lambda_1,\lambda_2,\cdots,\lambda_n\in [0,1]$ such that $\sum_{i=1}^{n}\lambda_i=1$, we have
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>
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> $$f(\sum_{i=1}^{n}\lambda_ix_i)\leq \sum_{i=1}^{n}\lambda_if(x_i)$$
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>
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> Proof:
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>
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> If $f$ is a convex function, there are three properties that useful for the proof:
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>
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> 1. $f''(x)\geq 0$ for all $x\in (a,b)$
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> 2. For any $x,y\in (a,b)$, $f(x)\geq f(y)+(x-y)f'(y)$ (Take tangent line at $y$)
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> 3. For any $x,y\in (a,b)$ and $0<\lambda<1$, we have $f(\lambda x+(1-\lambda)y)\leq \lambda f(x)+(1-\lambda)f(y)$ (Take line connecting $f(x)$ and $f(y)$)
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>
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> We use $f(x)\geq f(y)+(x-y)f'(y)$, we replace $y=\sum_{i=1}^{n}\lambda_ix_i$ and $x=x_j$, we have
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>
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> $$f(x_j)\geq f(\sum_{i=1}^{n}\lambda_ix_i)+(x_j-\sum_{i=1}^{n}\lambda_ix_i)f'(\sum_{i=1}^{n}\lambda_ix_i)$$
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>
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> We sum all the inequalities, we have
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>
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> $$
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\begin{aligned}
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\sum_{j=1}^{n}\lambda_j f(x_j)&\geq \sum_{j=1}^{n}\lambda_jf(\sum_{i=1}^{n}\lambda_ix_i)+\sum_{j=1}^{n}\lambda_j(x_j-\sum_{i=1}^{n}\lambda_ix_i)f'(\sum_{i=1}^{n}\lambda_ix_i)\\
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&\geq \sum_{j=1}^{n}\lambda_jf(\sum_{i=1}^{n}\lambda_ix_i)+0\\
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&=f(\sum_{j=1}^{n}\lambda_ix_j)
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\end{aligned}
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$$
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Since $\log$ is a concave function, by Jensen's inequality $f(\sum_{i=1}^{n}\lambda_ix_i)\leq \sum_{i=1}^{n}\lambda_if(x_i)$, we have
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Since $\log$ is a concave function, by Jensen's inequality $f(\sum_{i=1}^{n}\lambda_ix_i)\leq \sum_{i=1}^{n}\lambda_if(x_i)$, we have
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$$
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$$
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