Non Convexity

Non-convexity, a pervasive characteristic of many optimization problems in machine learning and other fields, presents significant challenges due to the presence of multiple local optima hindering the search for global solutions. Current research focuses on developing algorithms to efficiently navigate these complex landscapes, including methods tailored to bilevel optimization problems, min-max optimization with delays, and those leveraging techniques like graduated non-convexity or higher-order losses. Overcoming the difficulties posed by non-convexity is crucial for improving the performance and reliability of various machine learning models and algorithms, impacting areas such as neural network training, reinforcement learning, and robust optimization.