An Approach to Optimization of Spin Configuration in Spin-Glass Systems by Chaotic Neural Network Method

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概要

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• A chaotic neural network model with globally coupled map (GCM) is employed to optimize spin configuration in Ising spin-glass systems. It has been supposed in Ising spin glass systems that a vast number of metastable states exist, which make it difficult to find the ground state and low energy metastable states. Through our calculations for Ising spin systems, many such metastable states at zero temperature are obtained. From the results of the Sherrington-Kirkpatrick (SK) Ising model, we show the existence of a non-trivial tree structure among these low energy metastable states, which supports the existence of ultrametrical structure predicted by Mezard, Parisi, Sourlas, Thoulouse, and Virasoro.

• 社団法人日本物理学会の論文
• 1995-10-15

著者

• Yamashita Michiru

  Department Of Physics Osaka University

関連論文

• An Approach to Optimization of Spin Configuration in Spin-Glass Systems by Chaotic Neural Network Method

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