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首页> 外文期刊>Physical Review X >Rethinking Mean-Field Glassy Dynamics and Its Relation with the Energy Landscape: The Surprising Case of the Spherical Mixed p -Spin Model
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Rethinking Mean-Field Glassy Dynamics and Its Relation with the Energy Landscape: The Surprising Case of the Spherical Mixed p -Spin Model

机译:重新思考的意思 - 现场玻璃动态及其与能量景观的关系:球形混合P-Spin模型的令人惊讶的情况

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The spherical p-spin model is a fundamental model in statistical mechanics of a disordered system with a random first-order transition. The dynamics of this model is interesting both for the physics of glasses and for its implications on hard optimization problems. Here, we revisit the out-of-equilibrium dynamics of the spherical mixed p-spin model, which differs from the pure p-spin model by the fact that the Hamiltonian is not a homogeneous function of its variables. We consider quenches (gradient descent dynamics) starting from initial conditions thermalized in the high-temperature ergodic phase. Unexpectedly, we find that, differently from the pure p-spin case, the asymptotic states of the dynamics keep memory of the initial condition. The final energy is a decreasing function of the initial temperature, and the system remains correlated with the initial state. This dependence disproves the idea of a unique “threshold” energy level attracting dynamics starting from high-temperature initial conditions. Thermalization, which could be achieved, e.g., by an algorithm like simulated annealing, provides an advantage in gradient descent dynamics and, last but not least, brings mean-field models closer to real glass phenomenology, where such a dependence is observed in numerical simulations. We investigate the nature of the asymptotic dynamics, finding an aging state that relaxes towards deep, marginally stable minima. However, careful analysis rules out simple generalizations of the aging solution of the pure model. We compute the constrained complexity with the aim of connecting the asymptotic solution to the energy landscape.
机译:球形P-旋转模型是随机系统的统计机制统计机制的基本模型,随机转型。这种模型的动态对于眼镜的物理和对硬优化问题的影响是有趣的。在这里,我们重新审视球形混合P-旋转模型的平衡动力学,其与纯P-旋转模型不同的是,汉密尔顿人不是其变量的均匀函数。我们考虑从高温ergodic相中热化的初始条件开始淬火(梯度下降动力学)。出乎意料的是,我们发现,不同于纯P-旋转情况,动态的渐近状态保持初始条件的记忆。最终能量是初始温度的降低功能,并且系统保持与初始状态相关。这一依赖性消除了唯一的“阈值”能级吸引从高温初始条件开始的动态的想法。可以实现的热化,例如,通过模拟退火等算法在梯度下降动力学中提供了优点,并且最后但并非最不重要,使得更接近真实玻璃现象学的平均场模型,其中在数值模拟中观察到这种依赖性。我们调查渐近动力学的性质,找到一种放松深入,边际稳定的最小值的老化状态。然而,仔细分析规定了纯模型的老化溶液的简单概括。我们计算受限复杂性,目的是将渐近解决方案连接到能量景观。

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