Challenges In Sustainable Non-Convex Optimization For Deep Learning Applications

Abstract

Today, it is a common practice to use datasets to make prediction inference in data-driven worlds. Deep learning experiments success depends on the existence and diversity of datasets that can deliver accurate results in different domain. Of these, the primary datasets (e.g., time series data) are often of spectacular efficiency. So, NP-hard problems in this setting makes it also very challenging, often resulting in non-convex solutions. To solve these problems, the critical step is to convert NP-hard problems to P problems in order to reach the best results. In this research author focus on datasets where it is hard to obtain an optimal solution, pointing out that finding a global minimum might be difficult in many situations. It also touches on the issue of how common are non-convexity problems in DC-set systems and lists some possible directions a follow-up research would take to improve them to be more friendly to convex optimization. The field can better advance deep learning applications by tackling these obstacles to make predictive analytics more accurate and efficient. This research main goal is to increase understanding of the difficulties associated with non-convex calculations in time series and real-time problem-solving scenarios.