Mean Field Gradient Descent

Mean-field gradient descent (MFGD) is a class of algorithms that analyze the behavior of many interacting particles by approximating their collective dynamics using a single representative particle. Current research focuses on refining MFGD's application to challenging problems like finding Nash equilibria in continuous games and improving sampling efficiency in energy-based models, often employing variations like microcanonical MFGD or two-scale approaches. These advancements offer improved convergence properties and enhanced control over entropy loss, leading to more robust and efficient solutions in diverse fields such as finance and machine learning.