  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.
