高级算法 (Fall 2019)/Problem Set 2

• 作业电子版于2019/11/5 23:59 之前提交到邮箱 njuadvalg@163.com
• 每道题目的解答都要有完整的解题过程。中英文不限。

Problem 1

Let [math]\displaystyle{ X }[/math] be a real-valued random variable with finite [math]\displaystyle{ \mathbb{E}[X] }[/math] and finite [math]\displaystyle{ \mathbb{E}\left[\mathrm{e}^{\lambda X}\right] }[/math] for all [math]\displaystyle{ \lambda\ge 0 }[/math]. We define the log-moment-generating function as

[math]\displaystyle{ \Psi_X(\lambda):=\ln\mathbb{E}[\mathrm{e}^{\lambda X}] \quad\text{ for all }\lambda\ge 0 }[/math],

and its dual function:

[math]\displaystyle{ \Psi_X^*(t):=\sup_{\lambda\ge 0}(\lambda t-\Psi_X(\lambda)) }[/math].

Assume that [math]\displaystyle{ X }[/math] is NOT almost surely constant. Then due to the convexity of [math]\displaystyle{ \mathrm{e}^{\lambda X} }[/math] with respect to [math]\displaystyle{ \lambda }[/math], the function [math]\displaystyle{ \Psi_X(\lambda) }[/math] is strictly convex over [math]\displaystyle{ \lambda\ge 0 }[/math].

• Prove the following Chernoff bound:

[math]\displaystyle{ \Pr[X\ge t]\le\exp(-\Psi_X^*(t)) }[/math].

In particular if [math]\displaystyle{ \Psi_X(\lambda) }[/math] is continuously differentiable, prove that the supreme in [math]\displaystyle{ \Psi_X^*(t) }[/math] is achieved at the unique [math]\displaystyle{ \lambda\ge 0 }[/math] satisfying

[math]\displaystyle{ \Psi_X'(\lambda)=t }[/math]

where [math]\displaystyle{ \Psi_X'(\lambda) }[/math] denotes the derivative of [math]\displaystyle{ \Psi_X(\lambda) }[/math] with respect to [math]\displaystyle{ \lambda }[/math].

• Normal random variables. Let [math]\displaystyle{ X\sim \mathrm{N}(\mu,\sigma) }[/math] be a Gaussian random variable with mean [math]\displaystyle{ \mu }[/math] and standard deviation [math]\displaystyle{ \sigma }[/math]. What are the [math]\displaystyle{ \Psi_X(\lambda) }[/math] and [math]\displaystyle{ \Psi_X^*(t) }[/math]? And give a tail inequality to upper bound the probability [math]\displaystyle{ \Pr[X\ge t] }[/math].

• Poisson random variables. Let [math]\displaystyle{ X\sim \mathrm{Pois}(\nu) }[/math] be a Poisson random variable with parameter [math]\displaystyle{ \nu }[/math], that is, [math]\displaystyle{ \Pr[X=k]=\mathrm{e}^{-\nu}\nu^k/k! }[/math] for all [math]\displaystyle{ k=0,1,2,\ldots }[/math]. What are the [math]\displaystyle{ \Psi_X(\lambda) }[/math] and [math]\displaystyle{ \Psi_X^*(t) }[/math]? And give a tail inequality to upper bound the probability [math]\displaystyle{ \Pr[X\ge t] }[/math].

• Bernoulli random variables. Let [math]\displaystyle{ X\in\{0,1\} }[/math] be a single Bernoulli trial with probability of success [math]\displaystyle{ p }[/math], that is, [math]\displaystyle{ \Pr[X=1]=1-\Pr[X=0]=p }[/math]. Show that for any [math]\displaystyle{ t\in(p,1) }[/math], we have [math]\displaystyle{ \Psi_X^*(t)=D(Y \| X) }[/math] where [math]\displaystyle{ Y\in\{0,1\} }[/math] is a Bernoulli random variable with parameter [math]\displaystyle{ t }[/math] and [math]\displaystyle{ D(Y \| X)=(1-t)\ln\frac{1-t}{1-p}+t\ln\frac{t}{p} }[/math] is the Kullback-Leibler divergence between [math]\displaystyle{ Y }[/math] and [math]\displaystyle{ X }[/math].

• Sum of independent random variables. Let [math]\displaystyle{ X=\sum_{i=1}^nX_i }[/math] be the sum of [math]\displaystyle{ n }[/math] independently and identically distributed random variables [math]\displaystyle{ X_1,X_2,\ldots, X_n }[/math]. Show that [math]\displaystyle{ \Psi_X(\lambda)=\sum_{i=1}^n\Psi_{X_i}(\lambda) }[/math] and [math]\displaystyle{ \Psi_X^*(t)=n\Psi^*_{X_i}(\frac{t}{n}) }[/math]. Also for binomial random variable [math]\displaystyle{ X\sim \mathrm{Bin}(n,p) }[/math], give an upper bound to the tail inequality [math]\displaystyle{ \Pr[X\ge t] }[/math] in terms of KL-divergence.

Give an upper bound to [math]\displaystyle{ \Pr[X\ge t] }[/math] when every [math]\displaystyle{ X_i }[/math] follows the geometric distribution with a probability [math]\displaystyle{ p }[/math] of success.

Problem 2

An [math]\displaystyle{ n }[/math]-dimensional hypercube [math]\displaystyle{ Q_n }[/math] is a graph with [math]\displaystyle{ 2^n }[/math] vertices, where each vertex is represented by an [math]\displaystyle{ n }[/math]-bit vector, and there is an edge between two vertices if and only if their bit-vectors differ in exactly one bit.

Given an [math]\displaystyle{ n }[/math]-dimensional hypercube with some non-empty subset of vertices [math]\displaystyle{ S }[/math], which is called marked black. Let [math]\displaystyle{ f(u) }[/math] denote the shortest distance from vertex [math]\displaystyle{ u }[/math] to any black vertex. Formally,

[math]\displaystyle{ f(u) = \min_{v \in S}\mathrm{dist}_{Q_n}(u,v), }[/math]

where [math]\displaystyle{ \mathrm{dist}_{Q_n}(u,v) }[/math] denotes the length of the shortest path between [math]\displaystyle{ u }[/math] and [math]\displaystyle{ v }[/math] in graph [math]\displaystyle{ Q_n }[/math] .

Prove that if we choose [math]\displaystyle{ u }[/math] from all [math]\displaystyle{ 2^n }[/math] vertices uniformly at random, then, with high probability, [math]\displaystyle{ f(u) }[/math] can not deviate from its expectation too much:

[math]\displaystyle{ \mathrm{Pr}[|f(u) - \mathbb{E}[f(u)]| \geq t\sqrt{n \log n}] \leq n^{-c}. }[/math]

Give the relation between [math]\displaystyle{ c }[/math] and [math]\displaystyle{ t }[/math].

Problem 4

Let [math]\displaystyle{ U }[/math] be a universal set. We use [math]\displaystyle{ 2^U }[/math] to denote the collection of all subsets of [math]\displaystyle{ U }[/math]. Let [math]\displaystyle{ \mathcal{F} }[/math] be a family of hash functions, in which each hash function [math]\displaystyle{ h:2^U \rightarrow \{0,1\}^m }[/math] maps subsets of [math]\displaystyle{ U }[/math] to 0-1 vectors of length [math]\displaystyle{ m }[/math]. A locality sensitive hashing scheme is a distribution on a family [math]\displaystyle{ \mathcal{F} }[/math] of hash functions operating on [math]\displaystyle{ 2^U }[/math], such that for two subsets [math]\displaystyle{ A,B \in 2^U }[/math],

[math]\displaystyle{ (1) \qquad \Pr_{h\in\mathcal{F}}[h(A)=h(B)]=sim(A,B). }[/math]

Here [math]\displaystyle{ sim:2^U \times 2^U \rightarrow [0,1] }[/math] is called the similarity function. Given a hash function family [math]\displaystyle{ \mathcal{F} }[/math] that satisfies Equation (1), we will say that [math]\displaystyle{ \mathcal{F} }[/math] is a locality sensitive hash function family corresponding to similarity function [math]\displaystyle{ sim(\cdot,\cdot) }[/math].

• For any similarity function [math]\displaystyle{ sim(A,B) }[/math] that admits a locality sensitive hash function family as defined in Equation (1), prove that the distance function [math]\displaystyle{ d(A,B) \triangleq 1-sim(A,B) }[/math] satisfies triangle inequality, formally,

[math]\displaystyle{ \forall A,B,C \in 2^U :\quad d(A,B) + d(B,C) \geq d(A,C). }[/math]

• Show that there is no locality sensitive hash function family corresponding to Dice's and the Overlap coefficient. Dice's coefficient is defined as:

[math]\displaystyle{ sim_{Dice}(A,B)=\frac{2|A\cap B|}{|A| + |B|}. }[/math]

Overlap coefficient is defined as:

[math]\displaystyle{ sim_{Ovl}(A,B) = \frac{|A\cap B|}{\min(|A|, |B|)}. }[/math]

Hint: use the triangle inequality result.

• Construct a collection of hash function [math]\displaystyle{ \mathcal{B} }[/math] where [math]\displaystyle{ f : \{0,1\}^m \rightarrow \{0,1\} }[/math] for each [math]\displaystyle{ f \in \mathcal{B} }[/math], together with a probability distribution on [math]\displaystyle{ \mathcal{B} }[/math] such that

[math]\displaystyle{ \forall x, y \in \{0,1\}^m:\quad \Pr_{f \in \mathcal{B}}[f(x) = f(y)] = \begin{cases} 1 &\text{ if } x= y;\\ \frac{1}{2} &\text{ if } x \neq y. \end{cases} }[/math]

Then use the hash function family [math]\displaystyle{ \mathcal{B} }[/math] to prove the following result. Given a locality sensitive hash function family [math]\displaystyle{ \mathcal{F} }[/math] ([math]\displaystyle{ h:2^U \rightarrow \{0,1\}^m }[/math] for each [math]\displaystyle{ h \in \mathcal{F} }[/math]) corresponding to a similarity function [math]\displaystyle{ sim(A,B) }[/math], we can obtain a locality sensitive hash function [math]\displaystyle{ \mathcal{F}' }[/math] ([math]\displaystyle{ h':2^U \rightarrow \{0,1\} }[/math] for each [math]\displaystyle{ h' \in \mathcal{F}' }[/math]) corresponding to the similarity function [math]\displaystyle{ \frac{1+sim(A,B)}{2} }[/math].