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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018-01-29
摘要
We introduce and apply an efficient method for the precise simulation of 隨機 dynamical processes on locally treelike graphs. 網絡s with cycles are treated in the framework of the cavity method. Such models correspond, for example, to spin-glass systems, Boolean 網絡s, 神經 網絡s, or other technological, 生物, and 社會 網絡s. Building upon ideas from 量子 many-body theory, our approach is based on a matrix product approximation of the so-called edge messages-conditional probabilities of vertex variable trajectories. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the matrix product edge messages (MPEM) in truncations. In contrast to 蒙地卡羅 simulations, the algorithm has a better error 縮放律 and works for both single instances as well as the 熱力學 limit. We employ it to examine
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