6 papers
Inertial Primal Dual Dynamics with Hessian-driven Damping for Saddle Point Problems
Zepeng Wang, Juan Peypouquet
Featuring Hessian-driven damping, two inertial primal dual dynamical systems are proposed for solving smooth saddle point problems with bilinear coupling. For convex-concave functi…
Accelerated Backward Forward Method for Convex Optimization
Zepeng Wang, Juan Peypouquet
We analyze the convergence rate of an accelerated backward forward method for solving convex composite optimization problems. The method was developed by Taylor, Hendrickx and Glin…
Adaptive Accelerated Gradient Method for Smooth Convex Optimization
Zepeng Wang, Juan Peypouquet
We propose an adaptive accelerated gradient method for solving smooth convex optimization problems. The method incorporates a scheme to determine the step size adaptively, by means…
Fast convex optimization via inertial systems with asymptotically vanishing viscosity and Hessian-driven damping
Zepeng Wang, Juan Peypouquet
We study the convergence rate of a family of inertial algorithms, which can be obtained by discretization of an inertial system combining asymptotic vanishing viscous and Hessian-d…
Convergence Rate Analysis for Monotone Accelerated Proximal Gradient Method
Zepeng Wang, Juan Peypouquet
We propose a monotone accelerated proximal gradient method for solving convex composite optimization problems, guaranteeing nonincreasing function values along the iterates -- a pr…
A unified analysis on the speedup of accelerated gradient methods: An inertial dynamics approach
Zepeng Wang, Juan Peypouquet
Nesterov's Accelerated Gradient Method is one of the most popular first order optimization algorithms. When applied to -smooth, -strongly convex functions, it converges at a…