论文标题
局部最小值和分叉延迟对连续变量组合优化的影响
Effects of local minima and bifurcation delay on combinatorial optimization with continuous variables
论文作者
论文摘要
组合优化问题可以映射到Ising模型上，并且通常很难找到其基态。已经提出了许多有关这些问题的启发式方法，一种有前途的方法是使用连续变量。近年来，通过使用称为Cooherent Ising机器的参数振荡器实现了一种这样的算法。尽管已经通过许多实验证实了这些算法具有高性能，但与其他熟悉的算法（例如模拟退火）不同，它们的计算能力尚未得到充分研究。在本文中，我们提出了一种基于连续变量的简单启发式，其静态和动力学特性易于研究。通过对拟议算法的分析，我们发现在优化和分叉延迟的早期阶段，许多局部最小值降低了其在特定类别的ISING模型中的性能。
Combinatorial optimization problems can be mapped onto Ising models, and their ground state is generally difficult to find. A lot of heuristics for these problems have been proposed, and one promising approach is to use continuous variables. In recent years, one such algorithm has been implemented by using parametric oscillators known as coherent Ising machines. Although these algorithms have been confirmed to have high performance through many experiments, unlike other familiar algorithms such as simulated annealing, their computational ability has not been fully investigated. In this paper, we propose a simple heuristic based on continuous variables whose static and dynamical properties are easy to investigate. Through the analyses of the proposed algorithm, we find that many local minima in the early stage of the optimization and bifurcation delay reduce its performance in a certain class of Ising models.
