Projected Gradient Descent (PGD)

Projected Gradient Descent (PGD) is an optimization method used to find solutions within specific constraints. It works by taking small steps in the direction that reduces an error or cost function (gradient descent). After each step, it "projects" the solution back into the allowed set, ensuring it stays within the constraints. This process repeats until a suitable solution is found. PGD is common in machine learning, especially for training models with complex restrictions, helping to efficiently navigate toward optimal solutions while respecting boundaries.