Proximal algorithms are iterative optimization methods used to solve non-smooth convex optimization problems by breaking them into simpler subproblems, often involving the proximal operator. They are particularly effective in handling large-scale problems and are widely used in machine learning, signal processing, and image reconstruction due to their ability to efficiently manage complex constraints and regularization terms.