In this fast-paced world, intelligent systems need to make reliable, real-time decisions in response to changing conditions and unexpected disruptions. Mathematical optimization is instrumental in this process, but it must offer reliable computations and robust solutions, especially in the presence of uncertainty. In this talk, we will explore how we can harness data to develop optimization tools that can handle uncertainty and operate in real-time. We will investigate learning techniques such as clustering and differentiable optimization to design tractable, robust problem formulations. We will also discuss how to use machine learning to design efficient optimization algorithms for continuous and mixed-integer optimization, with a focus on guaranteeing convergence in real-time operations. By customizing problem formulations and solution algorithms to specific tasks, we will show how we can significantly enhance the computational performance and reliability of decision-making pipelines.
 

Zoom: https://us02web.zoom.us/j/83344185446?pwd=RHRGai91RkZQTjg0eEVRMWQ5WXFjZz09