Robust optimization is concerned with decision-making under uncertainty, where the emphasis
is on guaranteeing a maximal level of performance despite unknown-but-bounded uncertainty.
This course covers the essentials of robust optimization and applications to various areas of
engineering and machine learning.
Students will learn the basic techniques and be exposed to various ways one can model
uncertainty so that the robust optimization problem is easily solvable.
The course aims to equip students to use optimization for real-world problems in a way that is
resilient and reliable despite uncertainty.