稳健投资组合选择的并行最优化算法研究与实现

项目名称： 稳健投资组合选择的并行最优化算法研究与实现

项目编号： No.61272193

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 胡永宏

作者单位： 中央财经大学

项目金额： 80万元

中文摘要： 稳健投资组合选择是数量化投资管理领域中的一项关键技术,目前其在应用中亟需高性能算法与实现研究。本项目针对现实投资场景下的投资组合选择问题，基于稳健优化方法构建合理的最优化模型，结合模型结构设计高性能算法并研究其并行化策略；着重研究典型的（混合整数）二阶锥规划和病态非凸优化问题的并行算法；结合目前主流的高性能计算系统架构，利用并行计算技术多层级优化性能，实现对稳健投资组合计算的快速响应；产出具有自主知识产权的稳健投资组合选择模型工具集、算法库和求解器；实现千核级规模计算，并行效率逾60%。该研究由具体应用驱动，多学科渗透，瞄准了现代投资组合理论前沿，切中了具体应用瓶颈，在投资组合选择建模、（混合整数）二阶锥规划全局并行算法设计、异构环境下计算金融实现三方面将有创新。预期成果将促进相关学科理论发展、投资组合优化方法创新、高性能计算应用、以及计算技术与投资管理实践的融合，具有现实意义和应用价值。

中文关键词： 模型及输入集；并行算法及求解器；金融与计算；；

英文摘要： Robust portfolio optimization is one of the key techniques in quantitative investment management, and the practical applications of robust portfolio selection have strong demands for high-performance algorithms and implementations. In this project, we shall firstly model the practical portfolio selection problems reasonably based on the methodology of robust optimization, then study the effective algorithms to solve the optimization models, and finally explore and implement the parallel algorithms. Our research will focus on the parallel algorithms for (mixed-integer) second-order cone programming and ill-behaved nonconvex optimization problems arising in robust portfolio optimization. With the increasingly popular heterogeneous high-performance computing systems, we shall optimize the computational performance in multi levels and achieve the goals of almost real-time response to the calculations of robust portfolios, output the model toolbox, high-performance algorithmic library and solvers for robust portfolio selection. We shall scale the parallel computing to 1000 CPU cores and get the parallel efficiency over 60%. This project is motivated by the specific financial applications and combined with multiple disciplines. It targets the state-of-the-art of modern portfolio theory, and captures the practical comp

英文关键词： Model and Input Set；Parallel Algorithm and Solver；Finance and Calculation；；