IBM Research - Ireland Internship Project: Time-varying optimisation for large-scale complex systems - overview
Continuously varying optimisation programs have been considered as a natural extension of time-invariant ones when the cost function, the constraints, or both, change continuously in time. Time-varying (convex) optimisation has been shown to be the right framework for a number of control, signal processing, and machine learning problems. Additional application domains include robotics and traffic engineering. This internship is focused on the fundamental research challenge of devising novel optimisation algorithms for tracking the solution trajectory of a time-varying optimisation problem in real-time with minimal computational effort.
- Devising novel optimisation algorithms and proving their performance and properties. Apply the resulting algorithms to applications stemming from automotive.
- A formal background in applied mathematics or related (with a strong emphasis on convex and nonconvex optimisation) is required, as well as being able to prove theoretical results via mathematical proofs.
- Python coding skills are preferred.