论文标题
在不确定性下优化的简介 - 简短的调查
An introduction to optimization under uncertainty -- A short survey
论文作者
论文摘要
优化在各个领域的工程师和科学家都可以将问题转录为通用公式，并相对轻松地接受最佳解决方案。从航空航天到机器人技术的行业继续受益于优化理论的进步和相关的算法发展。如今，实时使用优化，以在安全关键情况下（例如自动驾驶车辆）作用。通过将不确定性纳入优化计划中，生产强大的解决方案变得越来越重要。本文提供了有关在不确定性下优化的最新技术的简短调查。本文以简要介绍了优化的主要类别而没有不确定性。本文的其余部分着重于处理核心和认知不确定性的不同方法。本文中讨论的许多应用都在控制领域。该调查文件的目的是简要介绍各种不同方法的最新技术，并将读者推荐给其他文献，以更深入地处理此处讨论的主题。
Optimization equips engineers and scientists in a variety of fields with the ability to transcribe their problems into a generic formulation and receive optimal solutions with relative ease. Industries ranging from aerospace to robotics continue to benefit from advancements in optimization theory and the associated algorithmic developments. Nowadays, optimization is used in real time on autonomous systems acting in safety critical situations, such as self-driving vehicles. It has become increasingly more important to produce robust solutions by incorporating uncertainty into optimization programs. This paper provides a short survey about the state of the art in optimization under uncertainty. The paper begins with a brief overview of the main classes of optimization without uncertainty. The rest of the paper focuses on the different methods for handling both aleatoric and epistemic uncertainty. Many of the applications discussed in this paper are within the domain of control. The goal of this survey paper is to briefly touch upon the state of the art in a variety of different methods and refer the reader to other literature for more in-depth treatments of the topics discussed here.
