针对无线传感器网络能耗不均匀的问题,提出了一种基于博弈论模型的能量平衡路由(Game theory-based energy balance routing,GTEBR)算法.GTEBR算法通过引入仲裁机制及自信概率,将不完全信息的静态博弈转换为完全但不完美的信息静态博弈...针对无线传感器网络能耗不均匀的问题,提出了一种基于博弈论模型的能量平衡路由(Game theory-based energy balance routing,GTEBR)算法.GTEBR算法通过引入仲裁机制及自信概率,将不完全信息的静态博弈转换为完全但不完美的信息静态博弈,采用静态博弈的方法解决问题.本文设计了适合传感器节点的解算机制,并对采用GTEBR算法后的传感器网络纳什均衡的存在性作出了证明.最后仿真实验表明,采用GTEBR算法具有良好的收敛性以及很好的性能.展开更多
As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could b...As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could be simulated so as to finally yield the expectation value of the asset price,which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations.In order to solve the above computational problem,quantum Monte Carlo(QMC)has been established and applied in the relevant systems such as European call options.In this work,both MC and QM methods are adopted to simulate European call options.Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer,the amplitude estimation(AE)algorithm is found to play a secondary role in accelerating the pricing of European options.More importantly,the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.展开更多
文摘针对无线传感器网络能耗不均匀的问题,提出了一种基于博弈论模型的能量平衡路由(Game theory-based energy balance routing,GTEBR)算法.GTEBR算法通过引入仲裁机制及自信概率,将不完全信息的静态博弈转换为完全但不完美的信息静态博弈,采用静态博弈的方法解决问题.本文设计了适合传感器节点的解算机制,并对采用GTEBR算法后的传感器网络纳什均衡的存在性作出了证明.最后仿真实验表明,采用GTEBR算法具有良好的收敛性以及很好的性能.
基金This work was financially supported by the National Natural Science Foundation of China Granted No.11764028。
文摘As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could be simulated so as to finally yield the expectation value of the asset price,which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations.In order to solve the above computational problem,quantum Monte Carlo(QMC)has been established and applied in the relevant systems such as European call options.In this work,both MC and QM methods are adopted to simulate European call options.Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer,the amplitude estimation(AE)algorithm is found to play a secondary role in accelerating the pricing of European options.More importantly,the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.
