Alternating Minimization

Alternating minimization is an iterative optimization technique that tackles complex problems by sequentially minimizing over subsets of variables. Current research focuses on applying this method to diverse areas, including tensor factorization, image processing (e.g., Euler Elastica models), and machine learning (e.g., meta-learning and dictionary learning), often incorporating techniques like ADMM or Newton methods to improve efficiency and convergence. These advancements lead to improved algorithms for tasks such as low-rank matrix approximation, signal reconstruction, and motion planning, demonstrating the broad applicability and impact of alternating minimization across various scientific and engineering disciplines.