Stochastic programming approach to optimization under uncertainty
Stochastic programming approach to optimization under uncertainty
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DOI：
10.1007/s10107-006-0090-4
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发表时间：
2008-03-01
影响因子：
2.7
通讯作者：
Shapiro, Alexander
中科院分区：
文献类型：
--
作者：
Shapiro, Alexander
在本文中，我们讨论两阶段和多阶段随机规划问题的计算复杂性和风险规避方法。我们认为，两阶段（例如线性）随机规划问题可以通过蒙特卡罗采样技术以合理的精度解决，而有迹象表明多阶段程序的复杂性随着阶段数量的增加而快速增长。我们讨论将连贯风险度量扩展到多阶段设置，特别是此类问题的动态规划方程。
In this paper we discuss computational complexity and risk averse approaches to two and multistage stochastic programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We discuss an extension of coherent risk measures to a multistage setting and, in particular, dynamic programming equations for such problems.