First-order primal-dual methods for nonsmooth nonconvex optimisation
arXiv:1910.00115 · doi:10.1007/978-3-030-03009-4_93-1
Abstract
We provide an overview of primal-dual algorithms for nonsmooth and non-convex-concave saddle-point problems. This flows around a new analysis of such methods, using Bregman divergences to formulate simplified conditions for convergence.
References in corpus (17)
- On a generalization of the iterative soft-thresholding algorithm for the case of non-separable penalty
- A primal-dual hybrid gradient method for non-linear operators with applications to MRI
- Relaxed Gauss-Newton methods with applications to electrical impedance tomography
- Preconditioned ADMM with nonlinear operator constraint
- Acceleration and global convergence of a first-order primal--dual method for nonconvex problems
- Testing and non-linear preconditioning of the proximal point method
- Inertial, corrected, primal-dual proximal splitting
- Block-proximal methods with spatially adapted acceleration
- Primal-dual proximal splitting and generalized conjugation in non-smooth non-convex optimization
- A Forward-Backward Splitting Method for Monotone Inclusions Without Cocoercivity
- A Generalization of the Chambolle-Pock Algorithm to Banach Spaces with Applications to Inverse Problems
- Acceleration of the PDHGM on strongly convex subspaces
- An entropic Landweber method for linear ill-posed problems
- Primal-dual block-proximal splitting for a class of non-convex problems
- Introduction to Nonsmooth Analysis and Optimization
- First- and Second-Order Analysis for Optimization Problems with Manifold-Valued Constraints
- Fenchel Duality Theory and A Primal-Dual Algorithm on Riemannian Manifolds