暗通道约束和交替方向乘子法优化的湍流图像盲复原

为提高湍流退化图像的复原效果,针对盲复原算法在最大后验概率框架下,使用梯度分布先验信息约束容易求得模糊平凡解的问题,提出了一种暗通道约束和交替方向乘子法优化的湍流图像盲复原算法。基于多尺度的思想,在每一层尺度上,对图像施加暗通道先验约束,对点扩散函数施加非负性约束和能量约束。对采用坐标下降法交替迭代估计当前尺度下的模糊核和图像,当达到最大尺度时,得到最终估计的模糊核。结合总变分模型,采用交替方向乘子法优化实现图像细节快速恢复。实验结果表明,新算法使用的先验信息约束,有利于得到清晰解,在总变分模型下能收敛到全局最优解,可以有效抑制图像复原过程中产生的伪迹,恢复出更好的目标图像细节。

步长优化技术在交直流系统潮流计算中的应用研究

统一迭代法是求解交直流电力系统潮流计算问题的主要方法之一,通过引入步长优化乘子调整迭代过程中的修正量步长,可以获得更好的潮流计算收敛特性。首先给出了在交流潮流计算中得到广泛应用的最优乘子、准最优乘子在交直流潮流计算中的实现方法,之后在准最优乘子基础上提出了混合乘子。为验证应用步长优化乘子后潮流计算算法的有效性,采用修改的中国电力科学研究院22节点、IEEE 118节点和IEEE300节点交直流系统进行了计算分析。数值计算结果表明,分别引入最优乘子、准最优乘子和混合乘子的3种改进算法在潮流可解时的收敛特性均优于常规算法,且在潮流无解时收敛过程均不发散;带混合乘子的改进算法比其它两种在收敛速度、收敛精度和鲁棒性方面更具优势。

乘子方法中的参数选择

Di Pillo和Grippo提出的含参数C＞O的增广Lagrangian函数中,使用了最大函数,该函数可能在无穷多个点处不可微．为了克服这个问题,濮定国在2004年提出了一类带新的NCP函数的乘子法．该方法在增广Lagrangian函数和原问题之间存在很好的等价性；同时该方法具有全局收敛性,且在适当假设下,具有超线性收敛率．但是在该方法中,要求参数C充分大．为了实现算法及提高算法效率,本文给出了一个有效选择参数C的方法．

运用启发式能量函数观点的无功裕度估算

该文利用启发式能量函数思想,分别构建2母线系统、多母线系统的能量函数模型。在此基础上,通过引入优化乘子的保留非线性潮流法(preserving nonlinear flow algorithm,PNFA)计算得到的系统各负荷节点临界电压来求取其补偿前后的临界能量值,然后根据各临界能量值得到补偿前后系统的无功裕度,并分析电容器并联补偿对系统电压稳定的影响及补偿前后无功负荷比例增长时各节点电压相角、负荷节点需耗能量值的变化趋势。对2母线系统、IEEE30母线系统的仿真结果与通过连续潮流的计算结果的对比验证该思路的正确性和可行性。

Multi-phase signal setting and capacity of signalized intersection

A capacity model of multi-phase signalized intersections is derived by a stopping-line method. It is simplified with two normal situations： one situation involves one straight lane and one left-turn lane; the other situation involves two straight lanes and one left-turn lane. The results show that the capacity is mainly relative to signal cycle length, phase length, intersection layout and following time. With regard to the vehicles arrival rates, the optimal model is derived based on each phase＇s remaining time balance, and it is solved by Lagrange multipliers. Therefore, the calculation models of the optimal signal cycle length and phase lengths are derived and simplified. Compared to the existing models, the proposed model is more convenient and practical. Finally, a practical intersection is chosen and its signal cycles and phase lengths are calculated by the proposed model.

移动通信系统中基站天线波束赋形设计

摘 要：基站天线的波束赋形设计是提高移动通信系统信道性能的有效手段之一。本文基于移动通信系统，建立了基站天线赋形的优化模型，给出求解具有多个复杂约束条件的乘子罚函数的迭代算法。以八单元线阵为例，设计出压低上半空间所有副瓣峰值，填充下半空间零陷的基站天线，验证了本文方法的正确性和有效性。该方法对于波束赋形问题具有一定的借鉴意义。

Accelerated Matrix Recovery via Random Projection Based on Inexact Augmented Lagrange Multiplier Method

In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.

Modified Augmented Lagrange Multiplier Methods for Large-Scale Chemical Process Optimization

Chemical process optimization can be described as large-scale nonlinear constrained minimization. The modified augmented Lagrange multiplier methods (MALMM) for large-scale nonlinear constrained minimization are studied in this paper. The Lagrange function contains the penalty terms on equality and inequality constraints and the methods can be applied to solve a series of bound constrained sub-problems instead of a series of unconstrained sub-problems. The steps of the methods are examined in full detail. Numerical experiments are made for a variety of problems, from small to very large-scale, which show the stability and effectiveness of the methods in large-scale problems.

Power Allocation for Energy Harvesting in Wireless Body Area Networks

Wireless Body Area Networks(WBANs) are expected to achieve high reliable communications among a large number of sensors.The outage probability can be used to measure the reliability of the WBAN.In this paper,we optimize the outage probability with the harvested energy as constraints.Firstly,the optimal transmit power of the sensor is obtained while considering a single link between an access point(AP) located on the waist and a sensor attached on the wrist over the Rayleigh fading channel.Secondly,an optimization problem is formed to minimize the outage probability.Finally,we convert the non-convex optimization problem into convex solved by the Lagrange multiplier method.Simulations show that the optimization problem is solvable.The outage probability is optimized by performing power allocation at the sensor.And our proposed algorithm achieves minimizing the outage probability when the sensor uses energy harvesting.We also demonstrate that the average outage probability is reduced with the increase of the harvested energy.

Optimization Model on Quadratic Programming Problem with Fuzzy

In this paper, the authors propose a computational procedure by using fuzzy approach to fred the optimal solution of quadratic programming problems. The authors divide the calculation of the optimal solution into two stages. In the first stage the authors determine the unconstrained minimization and check its feasibility. The second stage, the authors explore the feasible region from initial point to another point until the authors get the optimal point by using Lagrange multiplier. A numerical example is included to support as illustration of the paper.

Differences and relations of objectives, constraints, and decision parameters in the optimization of individual heat exchangers and thermal systems

Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.

A new look at the Lagrange method for continuous-time stochastic optimization

We formulate a Lagrange method for continuous-time stochastic optimization in an appropriate normed space by using a proper stochastic process as the Lagrange multiplier.The obtained optimality conditions are applied to different types of problems.Some examples selected from control theory and economic theory are studied to test and illustrate the potential applications of the method.