一种改进的非凸秩最小化算法及其在矩阵恢复中的应用

在分析现有处理矩阵恢复问题的非凸秩最小化算法的基础上,提出了一种基于超松弛迭代的改进算法,并给出了松弛因子ω的确定准则。仿真实验表明:在惩罚参数选取较大的情形下,改进算法较原算法具有更快的收敛速度及更高的收敛精度,同时展示了基于非凸秩最小化算法的矩阵恢复技术在图像去噪中的应用。     

电子商务环境下物流配送中心选址决策与评价方法研究

电子商务环境下的物流配送中心选址涉及到多方面的因素,是一项复杂的系统工程。为此,提出了定量化的启发式算法来初步确定电子商务环境下配送中心选址方案。方案的确定是为了决策,因此依据定量计算和定性分析的结果,采用协调分析方法来科学地决策符合电子商务环境下的最优选址方案。     

基于改进遗传量子算法的FIR数字滤波器设计

网上证券交易在各国已经得到广泛应用。由网上证券交易引起的变革对传统的交易方式、交易制度产生不可估量的影响力,直接冲击着证券市场、投资者和券商。本文试图从证券市场和券商的角度来分析网上证券交易给它们带来的影响,从而从更深层次上理解网上证券交易的意义,更好地使用它。     

LDPC码在因子图上的构造及其译码

LDPC码是一种可以接近香农限的线性分组码，可通过稀疏奇偶校验矩阵来构造。也可以用因子图来构成。根据LDPC码的不同构成方法至今已提出了数种不同的译码方法。本文介绍了基于因子图的LDPC码的构造方法，分析了和一积(SPA)译码算法的基本原理，最后详细讨论了用SPA算法对LDPC码进行译码的过程。     

信用风险测量指标体系研究

在现代商业银行经营中,信用风险是影响其安全高效运营的主要原因。信用风险管理中最重要的就是信用风险测量。自从20世纪80年代末期以来,人工智能技术如神经网络、专家系统也被应用于商业银行信用风险测量中。但预测指标的研究则相对滞后,成为研究的一个难点。     

MAP算法在Turbo码译码中的应用和研究进展

对Turbo码译码算法进行了综述,包括SOVA、MAP、LOG-MAP、MAX-LOG-MAP等算法,并对这几种算法进行了比较。同时根据近年来对Turbo码译码算法的研究,对几种新的译码算法进行了介绍和讨论。     

中铁快运北京分公司配送网络优化研究

随着社会经济的发展,货物运输呈现几大趋势,其中包括货物运输的物流化。而物流作业(装卸搬运、储存、运输、包装、流通、加工、信息处理等)的很大部分都是在物流节点上完成的,所以,配送中心的选址是否合理直接影响配送中心各项经济活动的成本,同时也关系到企业的长久发展。针对中铁快运公司的具体实例,提出可行的网络优化设计方案,为中铁快运的决策提供支持。     

贪心算法求解最少资源问题的探讨

面对资源的日益紧缺,如何有效合理的利用资源一直是专家学者研究和探讨的热点问题。最少资源问题是对初步资源规划问题的探讨,可以为多个资源组合规划问题的基础研究提供有效的参考作用。传统的回溯法穷举虽然能找到最少资源问题的最优解但其时间复杂度会高于o(n!),往往耗时太多,不能满足问题的及时性。提出了一种以最早开始时间为贪心策略的求解最少资源问题的贪心算法,不仅能够找到最优解,而且其时间复杂度仅为o(n2),极大提高了算法的效率。     

The impact of climate and price risks on agricultural land use and crop management decisions

This article aims to investigate the impacts of climate change and of lower and more volatile crop price levels as currently observed in the European Union (EU) on optimal management decisions, average income and income risks in crop production in Western Switzerland. To this end, a bioeconomic whole-farm model has been developed that non-parametrically combines the crop growth model CropSyst with an economic decision model using a genetic algorithm. The analysis focuses on the farm level, which enables us to integrate a wide set of potential adaptation responses, comprising changes in agricultural land use as well as crop-specific fertilization and irrigation strategies. Furthermore, the farmer's certainty equivalent is employed as objective function, which enables the consideration of not only impacts on average income but also impacts on income variability.The study shows that that the effects of EU crop prices on the optimal management decisions as well as on the farmer's certainty equivalent are much stronger than the effects of climate change. Furthermore, our results indicate that the impacts of income risks on the crop farm's optimal management schemes are of rather low importance. This is due to two major reasons: first, direct payments make up a large percentage of the agricultural income in Switzerland which makes Swiss farmers less vulnerable to market and climate volatility. Second, arable crop farms in Switzerland have by law to cultivate at least four different crops. Due to these diverse cropping systems and high government direct payments risk does neither under climate change, market liberalization nor combinations thereof, play a very decisive role in arable farming in Switzerland.     

Can NN-algorithms and macroeconomic data improve OLS industry returns forecasts?∗

Using an extensive range of macroeconomic indicators and a number of two-stage models mixing OLS and a non-parametric approach known as the nearest neighbour algorithm, the authors analyse the potential for improving forecasts of US industry returns over those built by OLS on industry-specific variables only. Basic performance is measured by the average cross-sectional correlation over time between the 55 forecasted returns and the realized returns across industries. Since investors and asset managers typically want a steady performance over time, the volatility of this cross-sectional correlation is further taken into account in an adaptation of the Sharpe Ratio. Strong evidence is found in favour of certain macroeconomic factors as dominant industry return predictors, and some two-stage models based either purely on OLS or a mix between OLS and the non-linear model can lift both cross-sectional forecasting correlation and Sharpe Ratio. However, viewed overall in relation to the benchmark OLS model, performance is not consistently improved by any particular model.