在证券市场中,指数跟踪是投资者进行资产配置和风险管理的重要手段。传统的指数跟踪方法往往有模型解释性差、计算复杂度高等问题。因此,探索新的算法和技术以提升指数跟踪的效果具有重要意义。本文考虑Lasso回归模型来进行变量选择,首...在证券市场中,指数跟踪是投资者进行资产配置和风险管理的重要手段。传统的指数跟踪方法往往有模型解释性差、计算复杂度高等问题。因此,探索新的算法和技术以提升指数跟踪的效果具有重要意义。本文考虑Lasso回归模型来进行变量选择,首先介绍了Lasso回归模型基本原理,然后利用邻近梯度算法求解回归系数,该解具有稀疏性,旨在众多的变量中精确选择有效的部分变量来预测证券指数。最后利用沪深300指数以及其成分股的收盘价格作为分析数据,得出小部分股票就可以达到几乎相同的拟合效果,通过实例说明该方法在证券指数跟踪中有一定的有效性和优越性,能够实现更稀疏的变量组合,给证券指数跟踪提供了新的思路和方法。In the security market, index tracking is an important means for investors to allocate assets and manage risks. Traditional exponential tracking methods often have the problems of poor model interpretation and high computational complexity. Therefore, it is of great significance to explore new algorithms and techniques to improve the effect of exponential tracking. In this paper, Lasso regression model is considered for variable selection. Firstly, the basic principle of Lasso regression model is introduced, and then the adjacent gradient algorithm is used to solve the regression coefficient. The solution is sparse, aiming at accurately selecting effective part of the variables to predict the stock index from many variables. Finally, using the closing prices of CSI 300 index and its component stocks as analysis data, a small number of stocks can achieve almost the same fitting effect. An example shows that the method has certain effectiveness and superiority in securities index tracking, and can realize sparser variable combination, which provides a new idea and method for securities index tracking.展开更多
随着互联网的快速发展,新型的商业运营模式——电子商务使得我们的生活越来越便捷。本文所考虑的推荐系统是电子商务技术中的一种,其是利用电子商务网站向客户提供商品信息和建议,帮助用户决定应该购买什么产品,模拟销售人员帮助客户完...随着互联网的快速发展,新型的商业运营模式——电子商务使得我们的生活越来越便捷。本文所考虑的推荐系统是电子商务技术中的一种,其是利用电子商务网站向客户提供商品信息和建议,帮助用户决定应该购买什么产品,模拟销售人员帮助客户完成购买过程。协调过滤推荐技术是推荐系统技术中的一种,其基本思想是用户可以根据兴趣进行分类,类似的用户有着非常相似的利益,可以通过协作用对目标用户接收信息,过滤其他用户使用的建议,且其算法一般可以分为基于记忆和基于模型两类。本文主要研究推荐系统中协调过滤技术的基于模型的算法,即矩阵补全问题在推荐系统中的应用。在本文中,我们构造了矩阵补全问题的非凸连续松弛模型,并运用加速的迭代阈值算法求解模型,然后运用真实数据检验模型和算法在推荐系统中的有效性。With the rapid development of the Internet, the new business operation model—electronic commerce makes our life more and more convenient. The recommender system considered in this paper is one of electronic commerce technologies, which uses e-commerce websites to provide customers with commodity information and suggestions, help users decide what products to buy, and simulate sales personnel to help customers complete the purchase process. Collaborative filtering recommended technology is one of the recommender system technologies, its basic idea is that the user can be classified according to interest, similar users have very similar interests, can receive information through collaboration to the target user, filtering other users use suggestions, and the algorithm can be divided into two categories based on memory and based on the model. This paper focuses on the model-based algorithm of collaborative filtering technique in recommender system, namely the application of matrix completion problem in recommender system. In this paper, we construct a non-convex continuous relaxation model for the matrix completion problem, and use the accelerated iterative threshold algorithm to solve the model, and then use the real data to test the effectiveness of the model and the algorithm in the recommender system.展开更多
本文探讨了线性规划在电子商务物流中的应用,特别是通过解决运输问题来优化物流配送。通过建立线性规划模型,并利用路径追踪算法进行求解,我们能够在满足供应和需求约束的前提下,找到最优的运输方案,以最小化总运输成本。本文还通过一...本文探讨了线性规划在电子商务物流中的应用,特别是通过解决运输问题来优化物流配送。通过建立线性规划模型,并利用路径追踪算法进行求解,我们能够在满足供应和需求约束的前提下,找到最优的运输方案,以最小化总运输成本。本文还通过一个实际案例展示了该方法的应用,并分析了其在电子商务环境下的有效性和优势。This study explores the application of linear programming in e-commerce logistics, particularly focusing on optimizing logistics distribution through solving the transportation problem. By constructing a linear programming model and applying the path tracking algorithm, we can find the optimal transportation plan that minimizes total transportation costs while satisfying supply and demand constraints. This paper demonstrates the application of this method through a real-world case and analyzes its effectiveness and advantages in the e-commerce environment.展开更多
文摘在证券市场中,指数跟踪是投资者进行资产配置和风险管理的重要手段。传统的指数跟踪方法往往有模型解释性差、计算复杂度高等问题。因此,探索新的算法和技术以提升指数跟踪的效果具有重要意义。本文考虑Lasso回归模型来进行变量选择,首先介绍了Lasso回归模型基本原理,然后利用邻近梯度算法求解回归系数,该解具有稀疏性,旨在众多的变量中精确选择有效的部分变量来预测证券指数。最后利用沪深300指数以及其成分股的收盘价格作为分析数据,得出小部分股票就可以达到几乎相同的拟合效果,通过实例说明该方法在证券指数跟踪中有一定的有效性和优越性,能够实现更稀疏的变量组合,给证券指数跟踪提供了新的思路和方法。In the security market, index tracking is an important means for investors to allocate assets and manage risks. Traditional exponential tracking methods often have the problems of poor model interpretation and high computational complexity. Therefore, it is of great significance to explore new algorithms and techniques to improve the effect of exponential tracking. In this paper, Lasso regression model is considered for variable selection. Firstly, the basic principle of Lasso regression model is introduced, and then the adjacent gradient algorithm is used to solve the regression coefficient. The solution is sparse, aiming at accurately selecting effective part of the variables to predict the stock index from many variables. Finally, using the closing prices of CSI 300 index and its component stocks as analysis data, a small number of stocks can achieve almost the same fitting effect. An example shows that the method has certain effectiveness and superiority in securities index tracking, and can realize sparser variable combination, which provides a new idea and method for securities index tracking.
文摘随着互联网的快速发展,新型的商业运营模式——电子商务使得我们的生活越来越便捷。本文所考虑的推荐系统是电子商务技术中的一种,其是利用电子商务网站向客户提供商品信息和建议,帮助用户决定应该购买什么产品,模拟销售人员帮助客户完成购买过程。协调过滤推荐技术是推荐系统技术中的一种,其基本思想是用户可以根据兴趣进行分类,类似的用户有着非常相似的利益,可以通过协作用对目标用户接收信息,过滤其他用户使用的建议,且其算法一般可以分为基于记忆和基于模型两类。本文主要研究推荐系统中协调过滤技术的基于模型的算法,即矩阵补全问题在推荐系统中的应用。在本文中,我们构造了矩阵补全问题的非凸连续松弛模型,并运用加速的迭代阈值算法求解模型,然后运用真实数据检验模型和算法在推荐系统中的有效性。With the rapid development of the Internet, the new business operation model—electronic commerce makes our life more and more convenient. The recommender system considered in this paper is one of electronic commerce technologies, which uses e-commerce websites to provide customers with commodity information and suggestions, help users decide what products to buy, and simulate sales personnel to help customers complete the purchase process. Collaborative filtering recommended technology is one of the recommender system technologies, its basic idea is that the user can be classified according to interest, similar users have very similar interests, can receive information through collaboration to the target user, filtering other users use suggestions, and the algorithm can be divided into two categories based on memory and based on the model. This paper focuses on the model-based algorithm of collaborative filtering technique in recommender system, namely the application of matrix completion problem in recommender system. In this paper, we construct a non-convex continuous relaxation model for the matrix completion problem, and use the accelerated iterative threshold algorithm to solve the model, and then use the real data to test the effectiveness of the model and the algorithm in the recommender system.
文摘本文探讨了线性规划在电子商务物流中的应用,特别是通过解决运输问题来优化物流配送。通过建立线性规划模型,并利用路径追踪算法进行求解,我们能够在满足供应和需求约束的前提下,找到最优的运输方案,以最小化总运输成本。本文还通过一个实际案例展示了该方法的应用,并分析了其在电子商务环境下的有效性和优势。This study explores the application of linear programming in e-commerce logistics, particularly focusing on optimizing logistics distribution through solving the transportation problem. By constructing a linear programming model and applying the path tracking algorithm, we can find the optimal transportation plan that minimizes total transportation costs while satisfying supply and demand constraints. This paper demonstrates the application of this method through a real-world case and analyzes its effectiveness and advantages in the e-commerce environment.
