$G$ Method in Action: Pivot$^{\text{+}}$ Algorithm for Self-avoiding Walk

Abstract

The pivot algorithm -- we also call it the pivot chain -- is an algorithm for approximately uniform sampling from $\Omega _{N},$ the set of $N$-step self-avoiding walks on $\mathbb{Z}^{d}$ ($N,$ $d\geq 1$). Based on this algorithm and the $G$ method, we construct another algorithm/chain, called the pivot$^{\text{+}}$ algorithm/chain, for approximately uniform sampling from $\Omega _{N},$ here, $N\geq 2$. The pivot$^{\text{+}}$ algorithm samples the pivot from the set $\left\{ 1,2,...,N-1\right\} $ according to the uniform probability distribution on this set while the pivot algorithm samples the pivot from the set $\left\{0,1,2,...,N-1\right\} $ according to the uniform probability distribution on this set, so, on the pivot, the pivot$^{\text{+}}$ algorithm is better than the pivot algorithm. Further, we obtain another important thing, namely, the pivot$^{\text{+}}$ algorithm/chain enters, at time $1$, a set $2d$ times smaller than $\Omega _{N},$ and stays forever in this set, so, at times $1,2,...$ we work with a chain having a state space $2d$ times smaller than $\Omega _{N}$. As to the speed of convergence, we conjecture that the pivot$^{\text{+}}$ algorithm/chain is faster than the pivot algorithm/chain.