A Lagrangian dual method with self-concordant barriers for multi-stage stochastic convex programming

• 成果类型: Article
• 署名作者: Zhao, GY
• 署名单位: National University of Singapore
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-003-0471-x
• 发表日期: 2005
• 页码: 1-24
• 关键词: interior-point methods; linear-programs; decomposition method; optimization; uncertainty; allocation; POLICY

摘要

This paper presents an algorithm for solving multi-stage stochastic convex nonlinear programs. The algorithm is based on the Lagrangian dual method which relaxes the nonanticipativity constraints, and the barrier function method which enhances the smoothness of the dual objective function so that the Newton search directions can be used. The algorithm is shown to be of global convergence and of polynomial-time complexity.