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离散投资组合问题的一种基于Bundle对偶搜索的精确算法
  • ISSN号：1006-6330
  • 期刊名称：应用数学与计算数学学报
  • 时间：0
  • 页码：83-91
  • 分类：O224[理学—运筹学与控制论;理学—数学] TP31[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]上海大学理学院数学系,上海 200444, [2]复旦大学管理学院,上海 200433
  • 相关基金：国家自然科学基金资助项目,项目批准号：70671064,70518001.
  • 相关项目：服务运营管理理论和应用研究
作者： 张世涛|高振星|孙小玲|Zhang Shitao~1 Gao Zhenxing~1 Sun Xiaoling~2 Depar|
关键词： 离散均值-方差模型, 拉格朗日松弛, Bundle方法, 次梯度方法, 分枝定界法, discrete mean-variance model, Lagrangian relaxation, Bundle method, subgradient method, branch-and-bound method
中文摘要：

本文提出了离散均值-方差投资组合模型的一种新的精确算法．该算法是一个基于拉格朗日松弛和Bundle对偶搜索的分枝定界算法．我们分别用随机产生的数据和美国股票市场的真实数据进行了数值实验，并与传统次梯度对偶搜索进行了比较，数值结果表明本文提出的算法对解决中小规模的离散投资组合问题是有效的．

英文摘要：

In this paper, we propose an exact algorithm for the discrete mean-variance portfolio selection model. The algorithm is of branch-and-bound method based on Lagrangian relaxation and Bundle dual search method. Numerical experiment is carried out for test problems with data from randomly generated and U.S. stock market. Comparison results with subgradient dual search method is also reported. Computational results show that the propsed method is efficient for solving small-to-medium scale discrete mean-variance portfolio selection problems.

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期刊信息
  • 《应用数学与计算数学学报》
  • 主管单位:上海市教育委员会
  • 主办单位:上海大学
  • 主编：马和平
  • 地址：上海市上大路99号121信箱上海大学期刊社
  • 邮编：200444
  • 邮箱：camc@oa.shu.edu.cn
  • 电话：021-66137602
  • 国际标准刊号：ISSN：1006-6330
  • 国内统一刊号：ISSN：31-1436/O1
  • 邮发代号:
  • 获奖情况:
  • 国内外数据库收录:
  • 美国数学评论（网络版）,德国数学文摘
  • 被引量:1282