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Author (down) Bitar, N.; Goles, E.; Montealegre, P. doi  openurl
  Title COMPUTATIONAL COMPLEXITY OF BIASED DIFFUSION-LIMITED AGGREGATION Type
  Year 2022 Publication Siam Journal On Discrete Mathematics Abbreviated Journal SIAM Discret. Math.  
  Volume 36 Issue 1 Pages 823-866  
  Keywords diffusion-limited aggregation; computational complexity; space complexity; NL-completeness; P-completeness  
  Abstract Diffusion-Limited Aggregation (DLA) is a cluster-growth model that consists in a set of particles that are sequentially aggregated over a two-dimensional grid. In this paper, we introduce a biased version of the DLA model, in which particles are limited to move in a subset of possible directions. We denote by k-DLA the model where the particles move only in k possible directions. We study the biased DLA model from the perspective of Computational Complexity, defining two decision problems The first problem is Prediction, whose input is a site of the grid c and a sequence S of walks, representing the trajectories of a set of particles. The question is whether a particle stops at site c when sequence S is realized. The second problem is Realization, where the input is a set of positions of the grid, P. The question is whether there exists a sequence S that realizes P, i.e. all particles of S exactly occupy the positions in P. Our aim is to classify the Prediciton and Realization problems for the different versions of DLA. We first show that Prediction is P-Complete for 2-DLA (thus for 3-DLA). Later, we show that Prediction can be solved much more efficiently for 1-DLA. In fact, we show that in that case the problem is NL-Complete. With respect to Realization, we show that restricted to 2-DLA the problem is in P, while in the 1-DLA case, the problem is in L.  
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  ISSN 0895-4801 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000778502000037 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1558  
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