An introduction to optimization under uncertainty -- A short survey

TL;DR

This survey reviews current methods for optimization under uncertainty, emphasizing techniques for handling aleatoric and epistemic uncertainties, with applications mainly in control systems and autonomous decision-making.

Contribution

It provides a concise overview of the state of the art in optimization under uncertainty, highlighting various approaches and their applications in control and autonomous systems.

Findings

Various methods for handling uncertainty are summarized

Applications in control systems are emphasized

The survey points to further literature for detailed study

Abstract

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.