A. Yes, gradient descent can work on multi-output functions, which are functions that return more than one output variable. In the context of machine learning and optimization, a multi-output function might represent a scenario where you're trying to predict multiple target variables (outputs) simultaneously from a given set of input variables. Gradient descent, a popular optimization algorithm used to minimize the loss function in machine learning models, can be adapted to handle such multi-output scenarios.