A Fully Adaptive Strategy for Hamiltonian Cycles in the Semi-Random Graph Process

TL;DR

This paper introduces an adaptive strategy for the semi-random graph process that efficiently constructs Hamiltonian cycles, significantly narrowing the bounds between the minimum and maximum number of rounds needed.

Contribution

The paper presents a new adaptive strategy for creating Hamiltonian cycles in the semi-random graph process, improving bounds and reducing the gap between known upper and lower limits.

Findings

Achieves Hamiltonian cycle in less than 2.01678n rounds.

Proves it cannot be done in fewer than 1.26575n rounds.

Reduces the gap between bounds from 1.39162 to 0.75102.

Abstract

The semi-random graph process is a single player game in which the player is initially presented an empty graph on $n$ vertices. In each round, a vertex $u$ is presented to the player independently and uniformly at random. The player then adaptively selects a vertex $v$, and adds the edge $uv$ to the graph. For a fixed monotone graph property, the objective of the player is to force the graph to satisfy this property with high probability in as few rounds as possible. We focus on the problem of constructing a Hamiltonian cycle in as few rounds as possible. In particular, we present an adaptive strategy for the player which achieves it in $\alpha n$ rounds, where $\alpha < 2.01678$ is derived from the solution to some system of differential equations. We also show that the player cannot achieve the desired property in less than $\beta n$ rounds, where $\beta > 1.26575$. These results improve the previously best known bounds and, as a result, the gap between the upper and lower bounds is decreased from 1.39162 to 0.75102.