Minimization Algorithm

Minimization algorithms are optimization techniques aiming to find the lowest point (minimum) of a function, crucial for solving numerous problems across science and engineering. Current research focuses on improving the efficiency and convergence guarantees of algorithms like alternating minimization, often applied to complex models such as those involving matrix factorization, and extending their applicability to non-convex and high-dimensional problems. These advancements have significant implications for diverse fields, including machine learning (e.g., training deep neural networks and enhancing privacy), signal processing (e.g., image restoration), and robotics (e.g., optimizing robot control).