加权整体变分图像去噪模型的多重网格方法

加权整体变分图像去噪模型是整体变分模型图像去噪模型的改进,能更好的复原图像。在求解整体变分图像去噪模型中,对偶方法比原方法能更好的处理震荡和非光滑区域。为了建立加权整体变分模型对偶方法的快速算法,本文首先提出用多重网格方法对加权整体变分模型的对偶方程进行求解;然后对提出的多重网格方法进行收敛性分析,揭示出收敛困难的原因;最后为了达到收敛,对多重网格方法进行改进。实验结果表明,改进的多重网格方法比半隐式对偶方法收敛更快。     

用混合蚁群算法求解多工序订单生产计划

多工序订单生产排序问题,是一类典型的组合优化问题。采用混合蚁群算法,对一种多工序订单模型进行建模求解,并给出了详细的算法步骤。通过用不同数量的订单、工序组合的数据进行模拟计算与结果比较,证明了混合蚁群算法在求解此类的问题的有效性以及良好的鲁棒性。     

JIT环境下多工序订单生产排序模型及改进蚁群算法应用研究

订单排序问题是一类典型的组合优化问题,采用改进蚁群算法对一种具有多生产工序和JIT交货的订单模型进行建模求解,给出了详细的算法步骤,通过仿真计算和结果分析,与模拟退火算法和基本蚁群算法进行对比,证明了本算法的有效性。     

两种常用的射线追踪方法介绍

本文介绍了在工程中两种常用的射线追踪方法。分别对其原理、特点、过程及实现作了详细介绍。     

递归方程的分析

递归方程是在算法设计与分析过程中,提炼出的一种递归型问题的运算模型,这种运算模型通常需要经过理论上的时间复杂度和空间复杂度分析,才能进一步确定算法的可行性。文章对递归方程进行了归类,并分别阐述其求解方法,为分析具有递归特性问题的时间复杂度和空间复杂度提供了一种快速、有效的分析方法。     

基于模拟退火理论求解库存路径问题的模型与算法研究

借用模拟退火算法的理论和方法,分析与建立m-to-1库存路径模型,利用启发式算法制定求解流程,并对关键环节进行了详细的阐述.为了证明算法的正确性与先进性,结合算例对所提出的模型与算法进行了分析,验证了提出的模型与算法的可行性及在所求解问题的规模上具有先进性.     

一种增强型指数追踪模型设计及应用

研究目标：构建了可以调节追踪误差和超额收益的增强型指数追踪模型,并给出了广义最小角度回归算法（GLARS）,用以计算调节参数作用下模型解的折中路径。研究方法：通过模拟数据和五组世界主要股票市场指数的历史数据,对本文提出的模型和算法与同类模型和算法进行了性能比较;同时追踪上证50指数构建若干稀疏且稳定的资产组合模型,通过信息比率等指标对投资组合进行评价。研究发现：本文构建的模型可用以构造权衡追踪效果和超额收益,且稀疏的资产组合,GLARS算法相对传统预设参数的算法具有良好的求解能力和计算速度。研究创新：引入调节参数平衡追踪效果和超额收益,并针对中国股票市场的特点,在增强型指数追踪模型施加非负约束;GLARS算法可遍历所有折中意义下的最优解。研究价值：本文提出的增强型指数追踪模型在国内具有较强适用性,在保证资产稀疏性的前提下可以得到超额收益,同时丰富了目前投资组合中的方法论研究。     

基于Pareto遗传算法的仓库箱装军用物资垛位优化

垛位是否合理是影响后方仓库中箱装军用物资供应保障能力的一个重要因素。首先根据后方仓库中箱装军用物资堆垛优化原则和储存策略,建立了基于收发频率和同一性原则的多目标优化模型—箱装军用物资垛位优化数学模型;然后通过分析模型,提出采用Pareto遗传算法对垛位优化问题进行了求解,并给出求解的详细步骤;最后选用一个实例进行了应用,证明了该模型的合理性和算法的有效性。     

基于Pareto遗传算法的仓库箱装军用物资垛位优化

垛位是否合理是影响后方仓库中箱装军用物资供应保障能力的一个重要因素.首先根据后方仓库中箱装军用物资堆垛优化原则和储存策略,建立了基于收发频率和同一性原则的多目标优化模型-箱装军用物资垛位优化数学模型;然后通过分析模型,提出采用Pareto遗传算法对垛位优化问题进行了求解,并给出求解的详细步骤;最后选用一个实例进行了应用,证明了该模型的合理性和算法的有效性.     

基于量子粒子群算法的连杆机构的优化设计

粒子群算法是一种基于鸟群的智能优化方法,量子粒子群算法是对粒子群算法进行改进的算法,运算规则简单,收敛速度快,变量少,易于编程实现。对于多目标、多约束条件的四连杆机构优化设计,本文提出了一种基于量子粒子群算法求解的设计方法。经过仿真实践,能够有效求解,是求解四连杆机构优化问题的一个较好方案。     

On maximum likelihood estimation of stochastic frontier production models

The iterative algorithm suggested by Greene (1982) for the estimation of stochastic frontier production models does not necessarily solve the likelihood equations. Corrected iterative algorithms which generalize Fair's method (1977) and solve the likelihood equations are derived. These algorithms are compared with the Newton method in an empirical case. The Newton method is more time saving than these algorithms.     

基于 RFID物联网的生鲜食品冷链配送实时监控策略

文中针对生鲜食品冷链配送的过程信息是构建食品安全追溯平台的重要信息来源,提出了一个基于RFID物联网技术的冷链配送监控方案,探讨了基于RFID技术的冷链配送业务中装卸货作业自识别策略、RFID数据容错机制和去重过滤算法。利用面向物联网的冷链配送监控,可实时了解冷链配送环节中冷藏车设备运行性能、地理位置实时定位、货物种类与数量、温度等环境参数,实现对冷链配送全程的精细化监测与管理,该研究有助于提升冷链配送作业的自动化程度,有利于提高生鲜食品质量的监管溯源能力,对于保证食品安全具有重要现实意义。     

A Novel Compact Cat Swarm Optimization Based on Differential Method

ABSTRACT

A compact cat swarm optimization scheme (cCSO) is proposed in this paper, which is designed to solve application domains plagued with limited memory and less-computation power, as a member of cat swarm optimization algorithms (CSO), it composes of two sub-modes, i.e., tracing and seeking modes, so it keeps the same search logic of CSO. On the other hand, cCSO inherits the main feature of compact algorithms, a normal probabilistic model is used to represent the population of solutions instead of processing an actual population, which ensures the cCSO to have the modest memory requirement. The updating vector for the probabilistic model provides a clear moving direction for cats in next step. A cat without historical position and velocity is applied in the algorithm. When the cat is in seeking mode, it employs a differential operator to update the cat’s position, which makes it possible for the cat to have multiple searching directions. Experimental results show that cCSO has pretty performance compared with respect to some population-based testing benchmarks. And it also shows superior performance in convergence rate to some compact optimization algorithms. The case study of gray image segmentation proves that it suits for solving the optimization problem by limited hardware.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Forecasting exchange rates using feedforward and recurrent neural networks

In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample mean squared prediction error relative to the random walk model.     

Filtering and identification of Heston's stochastic volatility model and its market risk

We study the filtering problem for the stochastic volatility model of Heston by using the nonlinear estimation theory. To solve the estimation problem for the stochastic volatility process, we use the random time change method. The derived basic equation for the filtering is the so-called Zakai equation and its numerically realized algorithm is proposed with the aid of the splitting-up method. Regarding the European call option problem, the identification of the market price of the volatility risk is also studied. Some numerical simulation studies are demonstrated to show the advantage of the proposed method.     

A supply chain network equilibrium model with a smoothing newton solution scheme

ABSTRACT

Considering that a supply chain comprises several independent decision makers, a supply chain network equilibrium model that consists of manufacturers, retailers and consumers is developed. After analysing the optimal conditions of various decision makers in the model, the equilibrium condition is established as an equivalent, finite-dimensional variational inequality formulation and is solved by a smoothing Newton method. The global and quadratic convergence of the method is established. The numerical results show the rapid convergence of the method. Additionally, the rapid convergence of the smoothing Newton method is beneficial when solving a complicated network model in the real world.     

基于Gauss求积公式的Newton迭代法

利用Gauss数值积分公式构造牛顿迭代法的变形格式,得到牛顿迭代方法的三个新格式,并证明了它们的收敛阶都为3。通过matlab编程进行数值试验,结果表明三个新格式具有较好的收敛速度。它们丰富了非线性方程求根的方法,在理论上和应用上都有一定的价值。     

Asymmetric risk spillovers between Shanghai and Hong Kong stock markets under China’s capital account liberalization

In this paper, we investigate the asymmetric risk spillovers between Shanghai and Hong Kong stock markets under the backdrop of China’s capital account liberalization by measuring the Conditional Value-at-Risk (CoVaR) based on adjusted realized volatilities and variational mode decomposition based copula model. The empirical results show that, the asymmetric features of risk spillovers between the two markets are significant and manifest different states before and after the Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect schemes. More specifically, first, the downside risk spillovers from Hong Kong to Shanghai are significantly larger than its upside risk spillovers, while the risk spillovers from Shanghai to Hong Kong is on the contrary. Second, the short-run risk spillovers are more drastic than the long-run risk spillovers, except the risk spillovers from Shanghai to Hong Kong after the Shenzhen-Hong Kong Stock Connect scheme. Finally, by comparing the risk spillovers from two directions, the importance of Shanghai stock market gradually rises up with the implementations of Stock Connect schemes.     

The initial value problem for the trade cycle in euclidian space

[Ten Raa, 1984] has shown how arithmetics ideas carry over to distributions over space and can be used to solve open, static spatial problems such as the determination of urban equilibrium. This article extends the approach to dynamic spatial economics by tracing spatial distributions through time. It is shown that the basic ideas of ordinary differential equations carry over to the present context, provided that ‘functions’ are spatially distributed valued. The consequent differential equations for the distributions are solved. [Puu, 1982] spatial trade cycle model falls out as a special case and its associated initial value problem can now be completely solved.