The method of nonlinear conjugate gradients (NCG) is widely used in practice for unconstrained optimization, but it satisfies weak complexity bounds at best when applied to smooth convex functions. In ...

The Blue Curve: Represents the function f(x) = x⁴ - 4x² + 5; The Green-to-Blue Gradient Path: Shows the trajectory of the gradient descent algorithm; The Colored Points: Each point represents a step ...

Executive Summary: This project explores the Gradient Descent algorithm, a fundamental optimization technique in machine learning used to find the optimal parameters of a model. The project covers ...

The aim of this paper is to recall the conventional gradient method and discuss its performance when the target function neither is strongly convex nor has a Lipschitz continuous gradient. By ...

While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical applications ranging from ...