Non Convex Landscape

Non-convex optimization landscapes pose a significant challenge in many machine learning and scientific computing problems, hindering the efficient discovery of optimal solutions due to the presence of numerous suboptimal local minima and saddle points. Current research focuses on developing novel algorithms, such as those incorporating multiconvexity, higher-order losses, and generative neural networks, to navigate these complex landscapes more effectively. These advancements aim to improve the scalability and robustness of optimization methods across diverse applications, from large-scale sensor networks to training deep neural networks, ultimately leading to more efficient and accurate solutions.