Long-Path Problem

Overview

Given an undirected graph $G=(V,E)$ and a non-negative integer $B\in {\mathbb{N}}_0$, the Long-Path problem asks: Does there exist a path in $G$ of length exactly $B$?

Definitions

• A path is a sequence of vertices with no repetitions.
• A path of length $B$ contains $B+1$ distinct vertices.

NOTE

The Long-Path problem is NP-complete. It is closely related to the Hamiltonian Path and Hamiltonian Cycle problems. If we can solve the Long-Path problem in $t(n)$ for graph with $n$ nodes, then we can solve the Hamiltonian Path problem in $t(2n-2)+O(n^2)$

Futhermore:

$$G=(V,E)\text{ has a Hamiltonian Cycle}⟺G^{\prime }\text{ has path of length n}$$

Long Path - Hamiltonian Cycle Proof.excalidraw

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NOTE

By brute force we can solve the Long-Path problem in $O(n^{B+2})$ Goal: under $O(c^B)$ where $c$ is a constant

Randomized Approach: Color Coding

Key Idea

We randomly color the graph and search for a colorful path — a path where all $k$ vertices have distinct colors.

Let $k=B+1$. We seek a path of length $k-1$, using $k$ colors.

For $v\in V$ and $i\in \{0,\dots ,k-1\}$ define

$$P_i(v):=\left\{S\subseteq [k]||S|=i+1\wedge \exists \text{ a colorful path ending in }v\text{ that is colored exactly with }S\right\}$$

Goal: Compute all sets $P_i(v)$ for every $v\in V$ and every $i\in \{0,1,\dots ,k-1\}$ Example:

Colorful Path.excalidraw

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$P_0(v)=\{\{\gamma (v)\}\}$ (color of $v$) for $i\ge 1$ there is: $\exists \text{a colorful path that ends in }v\text{ that ends in S}$ $⟺\exists \text{ a neighbor }x\text{ of }v\text{ and a colorful path ending in }x\text{ that is colored with }S\setminus \{\gamma (v)\}$

Therefore:

$$P_i(v)=\bigcup _{x\in N(v)}\{R\cup \{\gamma (v)\}|R\in P_{i-1}(x)\wedge \gamma (v)\notin R\}$$

Computation of $P_i(v)$ for all $v\in V$, given $P_{i-1}(v)$ for all $v\in V$

$\text{BUNT}(G,i)$

• for all $v\in V$ do
  • $P_i(v)\leftarrow \varnothing$
  • for all $x\in N(v)$ do
    • for all $R\in P_{i-1}(x)$ with $\gamma (v)\notin R$ do
      • $P_i(v)\leftarrow P_i(v)\cup \{R\cup \{\gamma (v)\}\}$

Run Time

This algorithm checks for the existence of a colorful path of length $k-1$ in time

$$O(2^k\cdot k\cdot m)$$

where $m=|E|$

Probability That a Path Is Colorful

Suppose the graph contains a path $P$ of length $k-1$.
Randomly color each vertex independently with a color in $\{1,\dots ,k\}$.

There are $k^k$ possible colorings of the $k$ vertices on $P$.

Exactly $k!$ of them assign distinct colors to all vertices on $P$, i.e., make $P$ colorful.

So,

$$\mathrm{Pr}[P\text{ is colorful}]=\frac{k!}{k^k}$$

Using Stirling’s approximation:

$$k!\approx \sqrt{2\pi k}{\left(\frac{k}{e}\right)}^k$$

we get

$$\frac{k!}{k^k}\approx \sqrt{2\pi k}{\left(\frac{1}{e}\right)}^k$$

Therefore, a lower bound is

$$\frac{k!}{k^k}\ge {\left(\frac{1}{e}\right)}^k=e^{-k}$$

This means the chance of a particular path becoming colorful under a random coloring is at least $e^{-k}$.

Summary

Theorem

Let $G$ be a graph containing a path of length $k-1$.

1. A random coloring with $k$ colors produces a colorful path of length $k-1$ with probability

$$p\approx e^{-k}.$$

2. For repeated colorings with $k$ colors, the expected number of trials until a colorful path of length $k-1$ is obtained is

$$\frac{1}{p}\le e^k.$$

(See Geometric distribution)

Monte Carlo Algorithm for Long-Path

Let $k=B+1$.

Repeat the following $\lceil \lambda e^k\rceil$ times:

1. Randomly color the vertices of $G$ using $k$ colors.
2. Check whether the graph contains a colorful path of length $k-1$.
3. If such a path is found, return YES.

If no colorful path is found in any iteration, return NO.

Correctness

If the algorithm returns YES, the graph indeed contains a path of length $B$ (because colorful paths are simple paths by definition).

If the graph contains a path of length $B$, then with probability at least $e^{-k}$, one random coloring will make some such path colorful.

The probability that none of the $\lambda e^k$ trials succeeds is:

$$(1-e^{-k}{)}^{\lambda e^k}\le e^{-\lambda }$$

using the inequality

$$(1-x{)}^r\le e^{-xr}$$

So the error probability is at most $e^{-\lambda }$.

This is a Monte Carlo algorithm:
it may return NO incorrectly with small probability, but never returns YES incorrectly.

Run Time

Thus, the total runtime becomes:

$$O(\lambda \cdot e^k\cdot 2^k\cdot k\cdot m)=O(\lambda \cdot (2e{)}^k\cdot k\cdot m)$$

This is polynomial in $n$ when $k=O(\log n)$, i.e., when $B=O(\log n)$.