# Problem 11.1

## Longest-probe bound for hashing

Suppose that we use an open-addressed hash table of size $m$ to store $n \le m/2$ items.

a. Assuming uniform hashing, show that for $i = 1, 2, \ldots, n$, the probability is at most $2^{-k}$ that the $i$th insertion requires strictly more than $k$ probes.

b. Show that for $i = 1, 2, \ldots, n$, the probability is $\O(1/n^2)$ that the $i$th insertion requires more than $2\lg{n}$ probes.

Let the random variable $X_i$ denote the number of probes required by the $i$th insertion. You have shown in part (b) that $\Pr\{X_i > 2\lg{n}\} = \O(1/n^2)$. Let the random variable $X = \max_{1 \le i \le n} X_i$ denote the maximum number of probes required by any of the $n$ insertions.

c. Show that $\Pr\{X > 2 \lg{n}\} = \O(1/n)$

d. Show that the expected length $\E[X]$ of the longest probe sequence is $\O(\lg{n})$.

Alright.

### a. Number of probes

From the text we know that:

$$\Pr\{ X \ge i \} = \Pr\{ X > i - 1 \} = \frac{n}{m} \cdot \frac{n-1}{m-1} \cdots \frac{n-i+2}{m-i+2} \le \left( \frac{n}{m} \right)^{i-1} = \alpha^{i-1}$$

Since we know that $n \le m/2$, we know that:

$$\Pr\{X > k\} = \Pr\{X \ge k+1 \} \le \left( \frac{n}{m} \right)^k \le \left( \frac{m}{2m} \right)^k = \left( \frac{1}{2} \right)^k = 2^{-k}$$

### b. Insertion requiring more than $2\lg{n}$ probes

Well, just substitute in the previous with $k = 2\lg{n} = \lg{n^2}$:

$$\Pr\{X > \lg{n^2}\} \le 2^{-\lg{n^2}} = \frac{1}{n^2} = \O(1/n^2)$$

### c. Probability for longest probe

\begin{aligned} \Pr\{X > 2\lg{n}\} &= \Pr\{\bigcup_{i=1}^n \left( X_i > 2\lg{n} \right) \} && \\ &\le \sum_{i=1}^{n} \Pr\{X_i > 2\lg{n} \} && \text{since } \Pr\{A \cup B\} \le \Pr\{A\} + \Pr\{B\} \\ &\le \sum_{i=1}^{n} \frac{1}{n^2} && \text{because of (b)} \\ &= \frac{n}{n^2} \\ &= \O(1/n) \end{aligned}

### d. Expectation of the longest probe sequence

Here's a weird way to do it that I lifted from the Instructor's Manual after I gave up. The point is to split the expectation into two parts:

\begin{aligned} \E[X] &= \sum_{k=1}^{n} k \Pr \{ X = k \} \\ &= \sum_{k=1}^{\lceil 2\lg{n} \rceil} k \Pr\{X = k\} + \sum_{\lceil 2\lg{n} \rceil + 1}^n k \Pr\{X = k\} \\ &\le \sum_{k=1}^{\lceil 2\lg{n} \rceil} \lceil 2\lg{n} \rceil \cdot \Pr\{X = k\} + \sum_{\lceil 2\lg{n} \rceil + 1}^n n \cdot \Pr\{X = k\} \\ &= \lceil 2\lg{n} \rceil \sum_{k=1}^{\lceil 2\lg{n} \rceil} \Pr\{X = k\} + n \sum_{\lceil 2\lg{n} \rceil + 1}^n \Pr\{X = k\} \\ \end{aligned}

We can then simplify the two parts of the sum.

We know that $X$ takes only one value, so the sum of probabilities in the left part is at most $1$.

We know from (c) that the sum in the right part is $\O(n)$.

Thus:

\begin{aligned} \E[X] &\le \lceil 2 \lg{n} \rceil \cdot 1 + n \cdot \O(1/n) \\ &= \lceil 2 \lg{n} \rceil + \O(1) \\ &= \O(\lg{n}) \end{aligned}

### Take-away

This is basically saying that as long we keep half of the hash table empty, we can expect the longest probe to be no more than $\lg{n}$.