arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2009.01997cond-mat.stat-mechphysics.comp-ph
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张量重整化群的角转移矩阵全局优化

Global optimization of tensor renormalization group using the corner transfer matrix

Satoshi Morita, Naoki Kawashima

摘要:

提出了一种基于角点转移矩阵的全局优化张量网络重正化算法。由于角点转移矩阵重整化群方法更新了环境,因此不需要进行前向-后向迭代,而前向-后向迭代是其他全局优化方法中耗时的部分。此外,提出了一种进一步的近似方法,降低了粗粒度张量计算的收缩计算成本。我们的算法在二维中的计算时间尺度为键维数的六次方,而高阶张量重整化群和高阶第二重整化群方法具有七次方。我们在正方形格子上的伊辛模型中进行基准计算,并表明所提出的算法的求解时间比其他方法快。

英文摘要:

A tensor network renormalization algorithm with global optimization based on the corner transfer matrix is proposed. Since the environment is updated by the corner transfer matrix renormalization group method, the forward-backward iteration is unnecessary, which is a time-consuming part of other methods with global optimization. In addition, a further approximation reducing the order of the computational cost of contraction for the calculation of the coarse-grained tensor is proposed. The computational time of our algorithm in two dimensions scales as the sixth power of the bond dimension while the higher-order tensor renormalization group and the higher-order second renormalization group methods have the seventh power. We perform benchmark calculations in the Ising model on the square lattice and show that the time-to-solution of the proposed algorithm is faster than that of other methods.

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。

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