Gradient Projection

Gradient projection is an optimization technique used to find solutions within a constrained space, primarily addressing challenges in minimizing functions subject to various constraints. Current research focuses on applying gradient projection to diverse problems, including continual learning (mitigating catastrophic forgetting), multitask learning (managing conflicting gradients), and solving inverse problems (improving the accuracy and robustness of diffusion models). These applications highlight the method's significance in improving the efficiency and performance of machine learning models and solving complex optimization problems across various scientific and engineering domains.