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Optimization equips engineers and scientists in a variety of fields with theability to transcribe their problems into a generic formulation and receiveoptimal solutions with relative ease. Industries ranging from aerospace torobotics continue to benefit from advancements in optimization theory and theassociated algorithmic developments. Nowadays, optimization is used in realtime on autonomous systems acting in safety critical situations, such asself-driving vehicles. It has become increasingly more important to producerobust solutions by incorporating uncertainty into optimization programs. Thispaper provides a short survey about the state of the art in optimization underuncertainty. The paper begins with a brief overview of the main classes ofoptimization without uncertainty. The rest of the paper focuses on thedifferent methods for handling both aleatoric and epistemic uncertainty. Manyof 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 ina variety of different methods and refer the reader to other literature formore in-depth treatments of the topics discussed here.

An introduction to optimization under uncertainty -- A short survey