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arXiv 1202.0340physics.comp-phcond-mat.stat-mechq-bio.QM
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复杂地形上的单一温度蒙特卡罗优化

Single temperature for Monte Carlo optimization on complex landscapes

Denis Tolkunov, Alexandre V. Morozov

摘要:

我们提出了一种新的策略,蒙特卡罗(MC)优化崎岖的多维景观。该策略是基于查询的统计特性的景观,以找到在该温度下的平均首次穿越时间在当前区域的景观是最小化。因此,在其他算法,如模拟退火(SA)相比,我们明确匹配的温度计划的统计景观的不规则性。在这种统计在整个景观上近似相同的情况下,或者在非局部移动耦合景观的远距离部分的情况下,单温度MC将优于具有相同移动集的任何其他MC算法。我们还发现,在强各向异性库仑自旋玻璃和旅行商问题,唯一相关的统计(我们用来分配一个单一的MC温度)是在低能量漏斗的不规则性。我们的结果可以解释为什么自然界中的蛋白质折叠在室温下是有效的。

英文摘要:

We propose a new strategy for Monte Carlo (MC) optimization on rugged multidimensional landscapes. The strategy is based on querying the statistical properties of the landscape in order to find the temperature at which the mean first passage time across the current region of the landscape is minimized. Thus, in contrast to other algorithms such as simulated annealing (SA), we explicitly match the temperature schedule to the statistics of landscape irregularities. In cases where this statistics is approximately the same over the entire landscape, or where non-local moves couple distant parts of the landscape, single-temperature MC will outperform any other MC algorithm with the same move set. We also find that in strongly anisotropic Coulomb spin glass and traveling salesman problems, the only relevant statistics (which we use to assign a single MC temperature) is that of irregularities in low-energy funnels. Our results may explain why protein folding in nature is efficient at room temperatures.

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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