摘要
在这篇论文，一个新 branch-and-bound 算法在金融优化与圆许多限制为分离多因素公事包选择模型基于 Lagrangian 双松驰和连续松驰被建议。这个分离公事包模型整数是二次的编程问题。模型的可分离的结构被使用 Lagrangian 松驰和双搜索调查。算法能够从美国证券市场并且随机与数据解决真实世界的公事包问题的计算结果表演与多达 120 证券产生了测试问题。
In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.
基金
Project supported by the National Natural Science Foundation of China （Grant Nos.70518001. 70671064）
关键词
离散投资组合
多因素模型
分支定界算法
数学分析
portfolio optimization, discrete multi-factor model, Lagrangian relaxation and continuous relaxation, branch-and-bound method.
