Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization
It addresses optimization problems with nonsmooth functions, which are common in machine learning and engineering, offering a novel approach to improve convergence guarantees.
The paper tackles the challenge of nonsmooth optimization by proposing a multipoint generalization of gradient descent, proving linear convergence for max-of-smooth objectives, with experiments indicating broader applicability.
For strongly convex objectives that are smooth, the classical theory of gradient descent ensures linear convergence relative to the number of gradient evaluations. An analogous nonsmooth theory is challenging. Even when the objective is smooth at every iterate, the corresponding local models are unstable and the number of cutting planes invoked by traditional remedies is difficult to bound, leading to convergences guarantees that are sublinear relative to the cumulative number of gradient evaluations. We instead propose a multipoint generalization of the gradient descent iteration for local optimization. While designed with general objectives in mind, we are motivated by a ``max-of-smooth'' model that captures the subdifferential dimension at optimality. We prove linear convergence when the objective is itself max-of-smooth, and experiments suggest a more general phenomenon.