论文标题
爱德华兹 - 安德森模型中的全球最小深度
Global Minimum Depth In Edwards-Anderson Model
论文作者
论文摘要
在文献中，最常引用的数据是非常矛盾的，并且在2D Edwards-Anderson（2D EA）模型的全球最小值上没有共识。通过计算机模拟，借助精确的多项式Schraudolph-Kamenetsky算法，我们检查了2D EA-Type模型中的全局最小深度。我们发现全球最低深度对问题n的尺寸的依赖性，并在限制$ n \ to \ infty $中获得了其渐近值。我们认为，这些评估可以进一步用于检查机器学习和图像处理中经常使用的2D贝叶斯模型的行为。
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the help of exact polynomial Schraudolph-Kamenetsky algorithm, we examined the global minimum depth in 2D EA-type models. We found a dependence of the global minimum depth on the dimension of the problem N and obtained its asymptotic value in the limit $N\to\infty$. We believe these evaluations can be further used for examining the behavior of 2D Bayesian models often used in machine learning and image processing.
