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We study a restricted class of self-avoiding walks (SAW) which start at the origin (0, 0), end at $(L, L)$, and are entirely contained in the square $[0, L] times [0, L]$ on the square lattice ${mathbb Z}^2$. The number of distinct walks is known to grow as $lambda^{L^2+o(L^2)}$. We estimate $lambda = 1.744550 pm 0.000005$ as well as obtaining strict upper and lower bounds, $1.628 < lambda < 1.782.$ We give exact results for the number of SAW of length $2L + 2K$ for $K = 0, 1, 2$ and asymptotic results for $K = o(L^{1/3})$. We also consider the model in which a weight or {em fugacity} $x$ is associated with each step of the walk. This gives rise to a canonical model of a phase transition. For $x < 1/mu$ the average length of a SAW grows as $L$, while for $x > 1/mu$ it grows as $L^2$. Here $mu$ is the growth constant of unconstrained SAW in ${mathbb Z}^2$. For $x = 1/mu$ we provide numerical evidence, but no proof, that the average walk length grows as $L^{4/3}$. We also consider Hamiltonian walks under the same restriction. They are known to grow as $tau^{L^2+o(L^2)}$ on the same $L times L$ lattice. We give precise estimates for $tau$ as well as upper and lower bounds, and prove that $tau < lambda.$
Flory-Huggins theory is a mean field theory for modelling the free energy of dense polymer solutions and polymer melts. In this paper we use Flory-Huggins theory as a model of a dense two dimensional self-avoiding walk confined to a square in the squ
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We consider self-avoiding walks terminally attached to a surface at which they can adsorb. A force is applied, normal to the surface, to desorb the walk and we investigate how the behaviour depends on the vertex of the walk at which the force is appl
Self-avoidance is a common mechanism to improve the efficiency of a random walker for covering a spatial domain. However, how this efficiency decreases when self-avoidance is impaired or limited by other processes has remained largely unexplored. Her
We introduce an efficient nonreversible Markov chain Monte Carlo algorithm to generate self-avoiding walks with a variable endpoint. In two dimensions, the new algorithm slightly outperforms the two-move nonreversible Berretti-Sokal algorithm introdu