Homework 5

This homework covers the material from lectures 17 to 22

Due date: November 29th, 11:59pm Waterloo time.

PDF version of Homework 5.

LaTeX template, in case you want to write it in LaTeX.

Required Exercises

Remember that you are only required to turn in 5 out of the 6 exercises below.

Problem 1 - Competitive Analysis (20 points)

You have reached a river (modelled as a straight line) and must find a bridge to cross it. The bridge is at some integer coordinate upstream or downstream.

1. Give a 9-competitive deterministic algorithm for optimizing the total distance travelled up and downstream before you find the bridge.
2. Give a randomized 7-competitive algorithm for the problem

Problem 2 - Paging Algorithms (20 points)

A conservative algorithm is one that makes at most $k$ page faults on any consecutive subsequence of the input that contains at most $k$ pages. Here $k$ is the size of the cache.

1. Prove that LRU and FIFO are conservative
2. Prove that any conservative algorithm is k-competitive.

Problem 3 - Multiplicative Weights Update (20 points)

Here is a variation on the deterministic Weighted-Majority algorithm, designed to make it more adaptive.

1. Each expert begins, on the 1st day, with weight $1$.
2. We predict the result of a weighted-majority vote of the experts.
3. If an expert makes a mistake, we penalize it by dividing its weight by $2$, but only if its weight was at least $1/4$ of the average weight of experts.

Prove that in any contiguous block of trials (e.g., the block from 51st day through the 77th day), the number of mistakes made by the algorithm is at most $O(m+\log n)$, where $m$ is the number of mistakes made by the best expert in that block, and $n$ is the total number of experts.

Problem 4 - Distinct Elements (20 points)

Consider again the distinct elements problem that we saw in class. We are given a stream of elements $a_1,\dots ,a_n$ from our universe $U=\{0,1,\dots ,2^b-1\}$, possibly with repetitions, and we would like to know how many distinct elements are there in the sequence. Since we are in the streaming setting, we will make only one pass through the sequence above, and we have little memory.

We will now analyze a different algorithm for the distinct elements problem:

• Let $N>n$ be an integer
• Pick at random a function $h:U\to [N]$ from a strongly 2-universal family.
• Let $m:=min\{h(a_1),\dots ,h(a_n)\}$
• Output $N/m$

1. Suppose that $a_1,\dots ,a_n$ contains $k$ distinct elements. Show that $$Pr[\text{ algorithm outputs a number }>4k]\le {\displaystyle \frac{1}{4}}$$
2. Suppose that $a_1,\dots ,a_n$ contains $k$ distinct elements. Show that $$Pr[\text{ algorithm outputs a number }<k/4]\le {\displaystyle \frac{1}{4}}$$
3. Assuming that $U=[\mathsf{poly}(n)]$, what is the memory requirement of the algorithm above?

Problem 5 - Hardness of Approximation (20 points)

Given a graph $G$, an independent set is a set of vertices such that no two are neighbors. The Maximum Independent Set (MIS) problem is a famous NP-complete problem.

Interestingly, the complement of an independent set is a vertex cover, so the complement of the MIS is the minimum vertex cover. We’ve seen (twice) how to get a two-approximation for vertex cover. Even though it is complementary, the situation for MIS is much worse.

Early in the study of approximation hardness, MIS was shown to be MAX-SNP-hard, meaning there is some constant to within which it cannot be approximated (unless P = NP).

Suppose one has an $\alpha$-approximation algorithm for MIS. Consider the following graph product operation for a graph $G$. Create a distinct copy $G_v$ of $G$ for each vertex $v$ of $G$. Then connect up the copies as follows: if $(u,v)$ is an edge of $G$, then connect every vertex in $G_u$ to every vertex in $G_v$.

1. Prove that if there is an independent set of size $k$ in $G$, then there is an independent set of size $k^2$ in the product graph.
2. Prove that given an independent set of size $s$ in the product graph, one can find an independent set of size $\sqrt{s}$ in $G$.
3. Conclude from the MAX-SNP-hardness of MIS that MIS has no constant-factor approximation (unless P = NP).

Problem 6 - Proving that ${\omega }_{det}={\omega }_{mult}$ (20 points)

Given a matrix $A\in {\mathbb{Q}}^{N\times N}$ where $N=2^k$, prove that one can compute $det(A)$ in time $O(n^{\omega })$, where $\omega$ is the matrix multiplication exponent. You can assume that any matrix that you need to invert in the process is invertible.

Optional question: how would you remove the assumption given above?

Practice Problems

You are not required to submit the solution to these problems, but highly encouraged to attempt them for your own knowledge sake. :)

Problem 1

Suppose in the paging problem that the size of our cache is $k$ but the size of the cache for an offline algorithm is $\ell \le k$. Generalize the arguments from lecture 17 to show how competitive LRU will be in this scenario.

Problem 2

Prove that the general version of the MWU given in lecture 18 actually captures the basic version of MWU that we showed in lecture.