Mihailo Stojnic
This paper extends the large deviation framework for bilinearly indexed random processes to include atypical features and local entropies, enhancing the applicability of previous stationarized interpolation methods.
This research explores a mathematical framework for understanding complex random processes that are indexed in two dimensions, known as bilinearly indexed random processes. The authors build upon previous work by incorporating a concept called 'large deviation,' which helps in analyzing rare or atypical events within these processes. This is important because it allows for a better understanding of how unusual patterns or clusters of solutions arise in difficult optimization problems, which are often linked to computational challenges. The study also presents these findings in a simplified and elegant form, making them useful for further research and applications.
