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6 Disordered Spin Models
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Published:February 1999
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Abstract
In the previous few chapters of this book we have studied a variety of different Monte Carlo algorithms for simulating the using model under various conditions. In Chapter 3 we looked at the Metropolis algorithm, as it applies to the using model, and in Chapter 4 we looked at a number of other algorithms, most of which are designed to simulate the model more efficiently in the critical region, the region near the phase transition between paramagnetism and ferromagnetism. In the last chapter, we looked at a slight variation on the using model theme, the conserved-order-parameter using model, which can be used as a model of a gas, or of atoms diffusing on a surface. As we have seen, for different regimes of temperature or different variations of the model, different algorithms are appropriate. The trick is to tailor the algorithm to the type of problem we wish to tackle. However, all the algorithms we have seen so far eventually boil down to one thing: they generate a series of states, a Markov chain, in which each state is derived from the previous one in the chain, and attempt to contrive that each should appear with a probability proportional to its Boltzmann weight. In this chapter, for the first time, we will come across systems for which an approach of this kind doesn’t work. The problem, as we will see, is that for some systems, the so-called glassy systems, algorithms of this kind tend to get stuck in small regions of the state space from which they cannot escape. The reason for this is two-fold. First, the fact that each state is generated from the previous one in the chain means that there is a sense in which states are “close together”. There are states which it is easy to get to from the present one in a small number of moves, such as states which differ by the flipping of just a few spins for a single-spin-flip algorithm, and there are other states which you can only get to by making a large number of moves.
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