随机算法 (Spring 2014)/Problem Set 3
Problem 1
(Due to J. Naor.)
The Chernoff bound is an exponentially decreasing bound on tail distributions. Let [math]\displaystyle{ X_1,\dots,X_n }[/math] be independent random variables and [math]\displaystyle{ \mathbf{E}[X_i]=0 }[/math] for all [math]\displaystyle{ 1\le i\le n }[/math]. Define [math]\displaystyle{ X=X_1+X_2+\dots+X_n }[/math]. We can use the following two kinds of tail inequalities for [math]\displaystyle{ X }[/math].
- Chernoff Bounds:
- [math]\displaystyle{ \Pr[|X|\ge\delta]\le\min_{t\ge 0}\frac{\mathbf{E}[e^{t|X|}]}{e^{t\delta}} }[/math].
- [math]\displaystyle{ k }[/math]th-Moment Bound:
- [math]\displaystyle{ \Pr[|X|\ge\delta]\le\frac{\mathbf{E}[|X|^k]}{\delta^k} }[/math].
- Show that for each [math]\displaystyle{ \delta }[/math], there exists a choice of [math]\displaystyle{ k }[/math] such that the [math]\displaystyle{ k }[/math]th-moment bound is stronger than the Chernoff bound. (Hint: You may use the probabilistic method.)
- Why would we still prefer the Chernoff bound to the seemingly stronger [math]\displaystyle{ k }[/math]th-moment bound?
Problem 2
Given a binary string, define a run as a maximal sequence of contiguous 1s; for example, the following string
- [math]\displaystyle{ \underbrace{111}_{3}00\underbrace{11}_{2}00\underbrace{111111}_{6}0\underbrace{1}_{1}0\underbrace{11}_{2} }[/math]
contains 5 runs, of length 3, 2, 6, 1, and 2.
Let [math]\displaystyle{ S }[/math] be a binary string of length [math]\displaystyle{ n }[/math], generated uniformly at random. Let [math]\displaystyle{ X_k }[/math] be the number of runs in [math]\displaystyle{ S }[/math] of length [math]\displaystyle{ k }[/math] or more.
- Compute the exact value of [math]\displaystyle{ \mathbb{E}[X_k] }[/math] as a function of [math]\displaystyle{ n }[/math] and [math]\displaystyle{ k }[/math].
- Give the best concentration bound you can for [math]\displaystyle{ |X_k -\mathbb{E}[X_k]| }[/math].