.png){kind=icon}
返回
Augmented Lagrangian method for probabilistic optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s10479-011-0884-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We analyze nonlinear stochastic optimization problems with probabilistic constraints described by continuously differentiable non-convex functions. We describe the tangent and the normal cone to the level sets of the underlying probability function and provide new insight into their structure. Furthermore, we formulate fist order and second order conditions of optimality for these problems based on the notion of p-efficient points. We develop an augmented Lagrangian method for the case of discrete distribution functions. The method is based on progressive inner approximation of the level set of the probability function by generation of p-efficient points. Numerical experience is provided.
Keyword:
Stochastic programming
Probabilistic constraints
Chance constraints
Second-order optimality conditions
p-efficient points
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.5
论文数:
8.1K
被引数:
2.1W
机构
引用论文
.png){kind=icon}
查看更多内容