Matrix decomposition and Lagrangian dual method for discrete portfolio optimization under concave transaction costs  

作　　者：高振星 张世涛 孙小玲 

机构地区：[1]Department of Mathematics,College of Sciences,Shanghai University,Shanghai 200444,P.R.China [2]Department of Management Science,School of Management,Fudan University,Shanghai 200433,P.R.China

出　　处：《Journal of Shanghai University(English Edition)》2009年第2期119-122,共4页上海大学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos.70671064,70518001)

摘　　要：In this paper, the discrete mean-variance model is considered for portfolio selection under concave transaction costs. By using the Cholesky decomposition technique, the convariance matrix to obtain a separable mixed integer nonlinear optimization problem is decomposed. A brand-and-bound algorithm based on Lagrangian relaxation is then proposed. Computational results are reported for test problems with the data randomly generated and those from the US stock market.In this paper, the discrete mean-variance model is considered for portfolio selection under concave transaction costs. By using the Cholesky decomposition technique, the convariance matrix to obtain a separable mixed integer nonlinear optimization problem is decomposed. A brand-and-bound algorithm based on Lagrangian relaxation is then proposed. Computational results are reported for test problems with the data randomly generated and those from the US stock market.

关 键 词：portfolio optimization Cholesky decomposition concave transaction costs Lagrangian relaxation brand-andbound 

分 类 号：O224[理学—运筹学与控制论]