Projected Gradient Descent

Projected Gradient Descent (PGD) is an iterative optimization algorithm used to solve constrained optimization problems, primarily by repeatedly taking gradient steps and projecting the result onto a feasible set. Current research focuses on applying PGD within diverse contexts, including adversarial training for enhanced model robustness, calibration of complex models like finite element simulations, and generating adversarial examples for evaluating the security of large language models. The efficiency and adaptability of PGD make it a valuable tool across numerous fields, impacting areas such as machine learning security, medical image analysis, and the development of more efficient optimization techniques.