An inertial proximal alternating direction method of multipliers for nonconvex optimization
From MaRDI portal
Publication:5031320
DOI10.1080/00207160.2020.1812585zbMath1479.90165OpenAlexW3080748381MaRDI QIDQ5031320
Yufeng Zhang, Mian-Tao Chao, Jin-Bao Jian
Publication date: 18 February 2022
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207160.2020.1812585
nonconvex optimizationalternating direction method of multipliersnonsmoothKurdyka-Łojasiewicz propertyinertial proximal point method
Related Items
An inertial Bregman generalized alternating direction method of multipliers for nonconvex optimization ⋮ An iterative method based on ADMM for solving generalized Sylvester matrix equations ⋮ A symmetric splitting sequential quadratic optimization algorithm for two-block nonlinearly constrained nonconvex optimization ⋮ Global Complexity Bound of a Proximal ADMM for Linearly Constrained Nonseparable Nonconvex Composite Programming ⋮ Unnamed Item ⋮ Unnamed Item ⋮ An inertial proximal partially symmetric ADMM-based algorithm for linearly constrained multi-block nonconvex optimization problems with applications
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions
- Inertial Douglas-Rachford splitting for monotone inclusion problems
- An inertial Tseng's type proximal algorithm for nonsmooth and nonconvex optimization problems
- An inertial forward-backward algorithm for monotone inclusions
- iPiasco: inertial proximal algorithm for strongly convex optimization
- On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- On gradients of functions definable in o-minimal structures
- Introductory lectures on convex optimization. A basic course.
- Convergence of ADMM for multi-block nonconvex separable optimization models
- Global convergence of ADMM in nonconvex nonsmooth optimization
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- General inertial proximal gradient method for a class of nonconvex nonsmooth optimization problems
- Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)
- iPiano: Inertial Proximal Algorithm for Nonconvex Optimization
- Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems
- Inertial Proximal ADMM for Linearly Constrained Separable Convex Optimization
- Global Convergence of Splitting Methods for Nonconvex Composite Optimization
- Characterizations of Łojasiewicz inequalities: Subgradient flows, talweg, convexity
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Monotone Operators and the Proximal Point Algorithm
- Variational Analysis
- On the Minimizing Property of a Second Order Dissipative System in Hilbert Spaces
- Non-convex Optimization for Machine Learning
- Weak Convergence of a Relaxed and Inertial Hybrid Projection-Proximal Point Algorithm for Maximal Monotone Operators in Hilbert Space
- Convergence rate bounds for a proximal ADMM with over-relaxation stepsize parameter for solving nonconvex linearly constrained problems
- Convergence of alternating direction method for minimizing sum of two nonconvex functions with linear constraints
- A Dynamical Approach to an Inertial Forward-Backward Algorithm for Convex Minimization
- An overview of the estimation of large covariance and precision matrices
- Fast Alternating Direction Optimization Methods
- Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction
- Compressed sensing
- An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping