From the 1 of 17 linked papers with an AI index.
17 papers
Inertial Primal Dual Dynamics with Hessian-driven Damping for Saddle Point Problems
Zepeng Wang, Juan Peypouquet
The paper introduces two inertial primal‑dual dynamical systems with Hessian‑driven damping to solve smooth saddle‑point problems, proving fast convergence rates for both convex‑co…
Inertial forward-backward algorithm with exterior penalization and Tikhonov regularization
Siqi Qu, Juan Peypouquet, Mathias Staudigl
In a real Hilbertian setting, we develop in this paper numerical splitting techniques guaranteeing strong convergence to the least norm solution of constrained variational inequali…
Preconditioned primal-dual algorithms for saddle point problems: non-ergodic convergence rates
Huiyuan Guo, Juan José Maulén, Juan Peypouquet
We study a family of preconditioned primal dual algorithms for convex-concave saddle point problems by the dynamics introduced in \cite{apidopoulos2026preconditioned}. The proposed…
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…
Towards faster first order methods: A continuous-time model to interpolate between speed and function value restart
Juan José Maulén, Huiyuan Guo, Juan Peypouquet
We introduce a new restarting scheme for a continuous inertial dynamics with Hessian driven-damping, and establish a linear convergence rate for the function values along the resta…
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…