Retraction Based Method

Retraction-based methods encompass a range of techniques addressing diverse challenges across multiple scientific domains. Current research focuses on improving efficiency and convergence in optimization algorithms, particularly those operating under constraints like orthogonality, by developing retraction-free alternatives that avoid computationally expensive steps. Applications span robotics (e.g., designing efficient wing deployment mechanisms in microrobots), machine learning (e.g., developing faster algorithms for tasks involving generalized Stiefel manifolds), and even research integrity (e.g., using AI to predict article retractions based on social media data). These advancements offer significant potential for improving computational speed and efficiency in various fields while also contributing to enhanced research practices.