凸体均匀采样的超松弛击中-逃逸Monte Carlo方法及其在代谢网络分析中的应用
Overelaxed hit-and-run Monte Carlo for the uniform sampling of convex bodies with applications in metabolic network analysis
摘要：
从理论和应用的角度来看，高维凸区域的均匀采样是一个重要的计算问题。打了就跑的蒙特卡罗算法是目前已知的最有效的方法，其瓶颈之一在于难以从高维的小角落中逃脱。受统计力学中优化蒙特卡罗方法的启发，我们定义了一个新的算法，通过对打了就跑的动力学进行松弛。我们在高维单纯形上进行了数值模拟，以测试其性能，指出其改进的逃离角度的能力，并最终将其应用于代谢网络稳态动力学中的推理问题。
英文摘要：
The uniform sampling of convex regions in high dimension is an important computational issue, from both theoretical and applied point of view. The hit-and-run montecarlo 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 montecarlo methods used in statistical mechanics we define a new algorithm by overelaxing the hit-and-run dynamics. We made numerical simulations on high dimensional simplices 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
