.png){kind=icon}
返回
An algorithm for constrained nonlinear optimization under uncertainty
.png){kind=icon}
PRE
AI
DOI：10.1016/S0005-1098(98)00150-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper considers robust formulations for the constrained control of systems under uncertainty. The underlying model is nonlinear and stochastic. A mean-variance robustness framework is adopted. We consider formulations to ensure feasibility over the entire domain of the uncertain parameters. However, strict Feasibility may not always be possible, and can also be very expensive. We consider two alternative approaches to address feasibility. Flexibility in the operational conditions is provided via a penalty framework. The robust strategies are rested on a dynamic optimization problem arising from a chemical engineering application. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword:
uncertainty
robust optimization
risk management
mean-variance analysis
nonlinear programming
sequential quadratic programming
Goldstein-Levitin-Polyak algorithm
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。
