Self-avoiding walk

Self-avoiding walk on a 15×15 square lattice
Self-avoiding walk on a 20x20 square lattice, simulated using sequential Monte Carlo
Unsolved problem in mathematics:
Is there a formula or algorithm that can calculate the number of self-avoiding walks in any given lattice?

In mathematics, a self-avoiding walk (SAW) is a sequence of moves on a lattice (a lattice path) that does not visit the same point more than once. This is a special case of the graph theoretical notion of a path. A self-avoiding polygon (SAP) is a closed self-avoiding walk on a lattice. Very little is known rigorously about the self-avoiding walk from a mathematical perspective, although physicists have provided numerous conjectures that are believed to be true and are strongly supported by numerical simulations.

In computational physics, a self-avoiding walk is a chain-like path in R2 or R3 with a certain number of nodes, typically a fixed step length and has the property that it doesn't cross itself or another walk. A system of SAWs satisfies the so-called excluded volume condition. In higher dimensions, the SAW is believed to behave much like the ordinary random walk.

SAWs and SAPs play a central role in the modeling of the topological and knot-theoretic behavior of thread- and loop-like molecules such as proteins. Indeed, SAWs may have first been introduced by the chemist Paul Flory[1][dubiousdiscuss] in order to model the real-life behavior of chain-like entities such as solvents and polymers, whose physical volume prohibits multiple occupation of the same spatial point.

SAWs are fractals. For example, in d = 2 the fractal dimension is 4/3, for d = 3 it is close to 5/3 while for d ≥ 4 the fractal dimension is 2. The dimension is called the upper critical dimension above which excluded volume is negligible. A SAW that does not satisfy the excluded volume condition was recently studied to model explicit surface geometry resulting from expansion of a SAW.[2][clarification needed]

The properties of SAWs cannot be calculated analytically, so numerical simulations are employed. The pivot algorithm is a common method for Markov chain Monte Carlo simulations for the uniform measure on n-step self-avoiding walks. The pivot algorithm works by taking a self-avoiding walk and randomly choosing a point on this walk, and then applying symmetrical transformations (rotations and reflections) on the walk after the nth step to create a new walk.

Calculating the number of self-avoiding walks in any given lattice is a common computational problem. There is currently no known formula, although there are rigorous methods of approximation.[3][4]

  1. ^ P. Flory (1953). Principles of Polymer Chemistry. Cornell University Press. p. 672. ISBN 9780801401343.
  2. ^ A. Bucksch; G. Turk; J.S. Weitz (2014). "The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion". PLOS ONE. 9 (1): e85585. arXiv:1304.3521. Bibcode:2014PLoSO...985585B. doi:10.1371/journal.pone.0085585. PMC 3899046. PMID 24465607.
  3. ^ Hayes B (Jul–Aug 1998). "How to Avoid Yourself" (PDF). American Scientist. 86 (4): 314. doi:10.1511/1998.31.3301.
  4. ^ Liśkiewicz M; Ogihara M; Toda S (July 2003). "The complexity of counting self-avoiding walks in subgraphs of two-dimensional grids and hypercubes". Theoretical Computer Science. 304 (1–3): 129–56. doi:10.1016/S0304-3975(03)00080-X.