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The computational complexity of pattern formation

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Abstract

The computational complexity of diffusion-limited aggregation and fluid invasion in porous media is studied. The time requirements on an idealized parallel computer for simulating the patterns formed by these models are investigated. It is shown that these growth models are P-complete. These results provide strong evidence that such pattern formation processes are inherently sequential and cannot be simulated efficiently in parallel.

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References

  1. T. Vicsek,Fractal Growth Phenomena (World Scientific, Singapore, 1989).

    Google Scholar 

  2. J. Apostolakis, P. Coddington, and E. Marinari,Europhys. Lett. 17:189 (1992); R. C. Brower, P. Tamayo, and B. York,J. Stat. Phys. 63:73 (1991).

    Google Scholar 

  3. C. H. Bennett, inComplexity, Entropy, and the Physics of Information, Wojciech H. Zurek, ed. (Addison-Wesley, 1990).

  4. A. Gibbons and W. Rytter,Efficient Parallel Algorithms (Cambridge University Press, Cambridge, 1988).

    Google Scholar 

  5. S. A. Cook,Information Control 64:2 (1985).

    Google Scholar 

  6. R. Greenlaw, H. J. Hoover, and W. L. Ruzzo, A Compendium of Problems Complete for P, Department of Computer Science, University of New Hampshire, Technical Report TR 91-14 (1991).

  7. M. Jerrum and A. Sinclair,SIAM J. Computing, to appear.

  8. R. Ladner,SIGACT News 7:18 (1975).

    Google Scholar 

  9. M. R. Garey and D. S. Johnson,Computers and Intractability (Freeman, San Francisco, 1979).

    Google Scholar 

  10. F. Barahona,J. Phys. A: Math. Gen. 15:3241 (1982).

    Google Scholar 

  11. J. Machta,J. Phys. A: Math. Gen. 25:521 (1992).

    Google Scholar 

  12. D. A. Huse and C. L. Henley,Phys. Rev. Lett. 54:2708 (1985).

    Google Scholar 

  13. F. Barahona,J. Phys. A: Math. Gen. 18:L673 (1985).

    Google Scholar 

  14. S. Wolfram,Theory and Applications of Cellular Automata (World Scientific, Singapore, 1986).

    Google Scholar 

  15. D. J. A. Welsh, inDisorder in Physical Systems, G. R. Grimmett and D. J. A. Welsh, eds. (Oxford University Press, Oxford, 1990).

    Google Scholar 

  16. J.-D. Chen and D. Wilkinson,Phys. Rev. Lett. 55:1892 (1985).

    Google Scholar 

  17. D. Y. C. Chan, B. D. Hughes, L. Paterson, and C. Sirakoff,Phys. Rev. A 38:4106 (1988).

    Google Scholar 

  18. Z. Koza,J. Phys. A: Math. Gen. 24:4895 (1991).

    Google Scholar 

  19. T. A. Witten and L. M. Sander,Phys. Rev. Lett. 47:1400 (1981).

    Google Scholar 

  20. L. Goldschlager,SIGACT News 9:25 (1977).

    Google Scholar 

  21. R. Anderson and E. Mayr,Information Process. Lett. 24:121 (1987).

    Google Scholar 

  22. L. A. Levin,SIAM J. Comput. 15:285 (1986).

    Google Scholar 

  23. Y. Gurevich,J. Computer Syst. Sci. 42:346 (1991).

    Google Scholar 

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Machta, J. The computational complexity of pattern formation. J Stat Phys 70, 949–966 (1993). https://doi.org/10.1007/BF01053602

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  • DOI: https://doi.org/10.1007/BF01053602

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