# Random walks avoiding their convex hull with a finite memory

## TL;DR

This paper proves that a finite-memory version of a convex hull-avoiding random walk in multi-dimensional space is ballistic, providing explicit limiting speed in a specific 2D case, and discusses open problems.

## Contribution

It establishes ballisticity for the finite-memory model and explicitly computes the limiting speed for a special 2D case, advancing understanding of convex hull-avoiding walks.

## Key findings

- Finite-memory model is ballistic in all dimensions.
- Explicit limiting speed for 2D case with k=1: 8/(9π^2).
- Comments on open problems and conjectures.

## Abstract

Fix integers $d \geq 2$ and $k\geq d-1$. Consider a random walk $X_0, X_1, \ldots$ in $\mathbb{R}^d$ in which, given $X_0, X_1, \ldots, X_n$ ($n \geq k$), the next step $X_{n+1}$ is uniformly distributed on the unit ball centred at $X_n$, but conditioned that the line segment from $X_n$ to $X_{n+1}$ intersects the convex hull of $\{0, X_{n-k}, \ldots, X_n\}$ only at $X_n$. For $k = \infty$ this is a version of the model introduced by Angel et al., which is conjectured to be ballistic, i.e., to have a limiting speed and a limiting direction. We establish ballisticity for the finite-$k$ model, and comment on some open problems. In the case where $d=2$ and $k=1$, we obtain the limiting speed explicitly: it is $8/(9\pi^2)$.