Summary
Optimization serves as the mathematical engine powering modern artificial intelligence and complex decision-making systems. Many real-world challenges, however, ranging from managing energy grids to training machine learning models, involve mathematical landscapes that are jagged, unpredictable, and obscured by data noise. These irregularities often trap existing technologies in suboptimal or inefficient solutions. This project pursues a new generation of rigorous mathematical tools and stable algorithms designed to navigate these difficult landscapes with precision and speed. The research wil
