P Redont – Optimization Online

Fast convex optimization via inertial dynamics with Hessian driven damping

Published: 2016/01/26

We first study the fast minimization properties of the trajectories of the second-order evolution equation \begin{equation*} \ddot{x}(t) + \frac{\alpha}{t} \dot{x}(t) + \beta \nabla^2 \Phi (x(t))\dot{x} (t) + \nabla \Phi (x(t)) = 0, \end{equation*} where $\Phi : \mathcal H \to \mathbb R$ is a smooth convex function acting on a real Hilbert space $\mathcal H$, and … Read more

Fast convergence of inertial dynamics and algorithms with asymptotic vanishing damping

Published: 2015/10/28

Convex Optimization convex optimization, dynamical systems, fast convergent methods, gradient flows, inertial dynamics, nesterov method, vanishing viscosity

In a Hilbert space setting $\mathcal H$, we study the fast convergence properties as $t \to + \infty$ of the trajectories of the second-order differential equation \begin{equation*} \ddot{x}(t) + \frac{\alpha}{t} \dot{x}(t) + \nabla \Phi (x(t)) = g(t), \end{equation*} where $\nabla\Phi$ is the gradient of a convex continuously differentiable function $\Phi: \mathcal H \to \mathbb R$, … Read more