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      Entropy Inflection and Inaccessible Ground States: the Defensive Alliance Example

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          Abstract

          Lower temperature leads to a higher probability of visiting low-energy states. This intuitive belief is underlying most physics-inspired strategies for hard optimization problems. For instance, the popular simulated annealing (SA) is expected to approach a ground state if the temperature is dropped appropriately. Here we demonstrate that this belief is not always justified. Specifically, we employ the cavity method to the hard computational problem of minimum strong defensive alliance to discover a bifurcation in the solution space, induced by an inflection point in the entropy--energy profile. While SA follows the lower-free-energy but far-from-optimal branch, ground-state solutions are obtained by following the higher-free-energy branch within the same temperature range. The existence of an inflection point causes the anomalous behavior of energy decreasing with temperature and makes the ground states inaccessible. We introduce an energy-clamping strategy to obtain superior solutions following the higher-free-energy branch, overcoming the limitations of SA.

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          Most cited references23

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          A simple model of global cascades on random networks.

          The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascades-herein called global cascades-that occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.
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            Exchange Monte Carlo Method and Application to Spin Glass Simulations

            We propose an efficient Monte Carlo algorithm for simulating a ``hardly-relaxing" system, in which many replicas with different temperatures are simultaneously simulated and a virtual process exchanging configurations of these replica is introduced. This exchange process is expected to let the system at low temperatures escape from a local minimum. By using this algorithm the three-dimensional \(\pm J\) Ising spin glass model is studied. The ergodicity time in this method is found much smaller than that of the multi-canonical method. In particular the time correlation function almost follows an exponential decay whose relaxation time is comparable to the ergodicity time at low temperatures. It suggests that the system relaxes very rapidly through the exchange process even in the low temperature phase.
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              Simulated Tempering: A New Monte Carlo Scheme

              We propose a new global optimization method ({\em Simulated Tempering}) for simulating effectively a system with a rough free energy landscape (i.e. many coexisting states) at finite non-zero temperature. This method is related to simulated annealing, but here the temperature becomes a dynamic variable, and the system is always kept at equilibrium. We analyze the method on the Random Field Ising Model, and we find a dramatic improvement over conventional Metropolis and cluster methods. We analyze and discuss the conditions under which the method has optimal performances.
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                Author and article information

                Journal
                12 February 2018
                Article
                1802.04047
                560bb649-a85a-41b5-9675-e1c7457e094d

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                5 pages, supplementary notes enclosed
                cond-mat.stat-mech physics.comp-ph

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