在将类似问题总结为考虑风险阈值的物流基础设施网络布局模型(Logistics Infrastructure Network Model under the Risk Threshold:LINM-RT)的基础上,把物流网络上的设施分为两种:"不可靠物流设施"和"可靠物流设施"...在将类似问题总结为考虑风险阈值的物流基础设施网络布局模型(Logistics Infrastructure Network Model under the Risk Threshold:LINM-RT)的基础上,把物流网络上的设施分为两种:"不可靠物流设施"和"可靠物流设施"。模型考虑了设施的最优数量和布局方案,研究拟通过各个设施对消费者需求的配送情况和设施类型的不同给出不同设施的布局方案,分析中断风险概率和消费者的需求对布局产生的影响和表现。分析了在已知条件充足的情况下,如何将LINM-RT模型简化为经典的无设施容量限制设施布局问题模型,并用拉格朗日松弛算法快速地去求解LINM-RT模型。研究结果证明了风险阈值的存在,用算例进一步说明在不同风险概率下物流基础设施网络布局选择是不同的。展开更多
Accurate descriptions of matrix diffusion across the fracture/matrix interface are critical to assessing contaminant migration in fractured media. The classical transfer probability method is only applicable for relat...Accurate descriptions of matrix diffusion across the fracture/matrix interface are critical to assessing contaminant migration in fractured media. The classical transfer probability method is only applicable for relatively large diffusion coefficients and small fracture spacings, due to an intrinsic assumption of an equilibrium concentration profile in the matrix blocks. Motivated and required by practical applications, we propose a direct numerical simulation （DNS） approach without any empirical assumptions. A three-step Lagrangian algorithm was developed and validated to directly track the particle dynamics across the fracture/matrix interface, where particle＇s diffusive displacement across the discontinuity is controlled by an analytical, one-side reflection probability. Numerical experiments show that the DNS approach is especially efficient for small diffusion coefficients and large fracture spacings, alleviating limitations of the classical modeling approach.展开更多
协作通信技术可以有效获取空间分集,并进一步改善系统的性能。为最小化多中继解码转发(decode-and-forward,DF)协作通信系统的总功率,同时满足系统要求的数据传输速率,提出了一种基于Lagrange算法的中继选择和功率分配的联合优化方案。...协作通信技术可以有效获取空间分集,并进一步改善系统的性能。为最小化多中继解码转发(decode-and-forward,DF)协作通信系统的总功率,同时满足系统要求的数据传输速率,提出了一种基于Lagrange算法的中继选择和功率分配的联合优化方案。首先根据信源与中继的瞬时信道状态信息(channel state information,CSI)使信源的发送功率最小化,然后选择出最大的可解码中继集合,再利用Lagrange算法对中继功率进行优化,根据得到的信源和中继的功率分配,选择出最佳中继集合,达到最小化系统总功率的目的。仿真结果表明,相对于直传方案和机会中继(opportunistic relay selection,ORS)方案,本研究中总功率消耗明显减少。展开更多
针对大破损彩色纹理图像的修复问题,将TV-L1模型推广到非局部CTV-L1模型。该模型不仅包含非局部算子,同时还引入了CTV(color total variation)规则项,前者可以修复大破损纹理图像,后者充分考虑了彩色图像层与层之间的耦合关系,在处理彩...针对大破损彩色纹理图像的修复问题,将TV-L1模型推广到非局部CTV-L1模型。该模型不仅包含非局部算子,同时还引入了CTV(color total variation)规则项,前者可以修复大破损纹理图像,后者充分考虑了彩色图像层与层之间的耦合关系,在处理彩色图像时可以有效地保持边缘。为提高模型的运算效率,通过引入辅助变量和Lagrange乘子为其设计了相应的增广Lagrangian算法。数值实验结果证实所提出的模型在处理彩色图像时可以有效地保持边缘,同时去除图像中异常的不规则点。该研究可以推广到彩色纹理图像椒盐噪声去除及彩色纹理图像分割中。展开更多
This paper presents an augmented Lagrangian algorithm for nonlinear opti- mization of equality and bounded constraints. The method includes internal it- erations and outer iterations, which uses a trust region interio...This paper presents an augmented Lagrangian algorithm for nonlinear opti- mization of equality and bounded constraints. The method includes internal it- erations and outer iterations, which uses a trust region interior-point method in internal iteration. Under some conditions, the paper proves finite termination of internal iteration and analyses the local convergence of accelerating internal mini- mizer iterations. It also proves the global convergence of main algorithm when the approximate solution of internal minimizer is satisfied some conditions.展开更多
文摘在将类似问题总结为考虑风险阈值的物流基础设施网络布局模型(Logistics Infrastructure Network Model under the Risk Threshold:LINM-RT)的基础上,把物流网络上的设施分为两种:"不可靠物流设施"和"可靠物流设施"。模型考虑了设施的最优数量和布局方案,研究拟通过各个设施对消费者需求的配送情况和设施类型的不同给出不同设施的布局方案,分析中断风险概率和消费者的需求对布局产生的影响和表现。分析了在已知条件充足的情况下,如何将LINM-RT模型简化为经典的无设施容量限制设施布局问题模型,并用拉格朗日松弛算法快速地去求解LINM-RT模型。研究结果证明了风险阈值的存在,用算例进一步说明在不同风险概率下物流基础设施网络布局选择是不同的。
基金supported by the United States Department of Energythe Desert Research Institute IR&D Funds
文摘Accurate descriptions of matrix diffusion across the fracture/matrix interface are critical to assessing contaminant migration in fractured media. The classical transfer probability method is only applicable for relatively large diffusion coefficients and small fracture spacings, due to an intrinsic assumption of an equilibrium concentration profile in the matrix blocks. Motivated and required by practical applications, we propose a direct numerical simulation （DNS） approach without any empirical assumptions. A three-step Lagrangian algorithm was developed and validated to directly track the particle dynamics across the fracture/matrix interface, where particle＇s diffusive displacement across the discontinuity is controlled by an analytical, one-side reflection probability. Numerical experiments show that the DNS approach is especially efficient for small diffusion coefficients and large fracture spacings, alleviating limitations of the classical modeling approach.
文摘协作通信技术可以有效获取空间分集,并进一步改善系统的性能。为最小化多中继解码转发(decode-and-forward,DF)协作通信系统的总功率,同时满足系统要求的数据传输速率,提出了一种基于Lagrange算法的中继选择和功率分配的联合优化方案。首先根据信源与中继的瞬时信道状态信息(channel state information,CSI)使信源的发送功率最小化,然后选择出最大的可解码中继集合,再利用Lagrange算法对中继功率进行优化,根据得到的信源和中继的功率分配,选择出最佳中继集合,达到最小化系统总功率的目的。仿真结果表明,相对于直传方案和机会中继(opportunistic relay selection,ORS)方案,本研究中总功率消耗明显减少。
文摘针对大破损彩色纹理图像的修复问题,将TV-L1模型推广到非局部CTV-L1模型。该模型不仅包含非局部算子,同时还引入了CTV(color total variation)规则项,前者可以修复大破损纹理图像,后者充分考虑了彩色图像层与层之间的耦合关系,在处理彩色图像时可以有效地保持边缘。为提高模型的运算效率,通过引入辅助变量和Lagrange乘子为其设计了相应的增广Lagrangian算法。数值实验结果证实所提出的模型在处理彩色图像时可以有效地保持边缘,同时去除图像中异常的不规则点。该研究可以推广到彩色纹理图像椒盐噪声去除及彩色纹理图像分割中。
文摘This paper presents an augmented Lagrangian algorithm for nonlinear opti- mization of equality and bounded constraints. The method includes internal it- erations and outer iterations, which uses a trust region interior-point method in internal iteration. Under some conditions, the paper proves finite termination of internal iteration and analyses the local convergence of accelerating internal mini- mizer iterations. It also proves the global convergence of main algorithm when the approximate solution of internal minimizer is satisfied some conditions.
基金Supported by National Natural Science Foundation of China(7146100571462004)+4 种基金Guangxi Natural Science Foundation(2014GXNSFFA1180012012GXNSFAA0530132014GXNSFAA118010)Postdoctoral Science Foundation of China(13R214147002013M540372)
