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Over-relaxed hit-and-run Monte Carlo for the uniform sampling of convex bodies with applications in metabolic network biophysics

Author

Listed:
  • G. De Concini

    (Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA)

  • D. De Martino

    (Center for Life Nanoscience CLNS-IIT, P.le A.Moro 2, 00815 Rome, Italy)

Abstract

The uniform sampling of convex regions in high dimension is an important computational issue, both from theoretical and applied point of view. The hit-and-run Monte Carlo algorithms are the most efficient methods known to perform it and one of their bottlenecks relies in the difficulty of escaping from tight corners in high dimension. Inspired by optimized Monte Carlo methods used in statistical mechanics, we define a new algorithm by over-relaxing the hit-and-run dynamics. We made numerical simulations on high-dimensional simplexes and hypercubes in order to test its performances, pointing out its improved ability to escape from angles and finally apply it to an inference problem in the steady state dynamics of metabolic networks.

Suggested Citation

  • G. De Concini & D. De Martino, 2015. "Over-relaxed hit-and-run Monte Carlo for the uniform sampling of convex bodies with applications in metabolic network biophysics," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(01), pages 1-9.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:01:n:s0129183115500102
    DOI: 10.1142/S0129183115500102
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