Cover Image

An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-Block Composite Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Deng, Zhanwang, Deng, Kangkang, Hu, Jiang, Wen, Zaiwen
Format:	Preprint
Published:	2023
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An efficient primal dual semismooth Newton method for semidefinite programming
by: Deng, Zhanwang, et al.
Published: (2025)

SSNCVX: A primal-dual semismooth Newton method for convex composite optimization problem
by: Deng, Zhanwang, et al.
Published: (2025)

The Augmented Lagrangian Methods: Overview and Recent Advances
by: Deng, Kangkang, et al.
Published: (2025)

Riemannian EXTRA: Communication-efficient decentralized optimization over compact submanifolds with data heterogeneity
by: Wu, Jiayuan, et al.
Published: (2025)

A Customized Augmented Lagrangian Method for Block-Structured Integer Programming
by: Wang, Rui, et al.
Published: (2024)

A Retraction-free Method for Nonsmooth Minimax Optimization over a Compact Manifold
by: Aybat, Necdet Serhat, et al.
Published: (2025)

An Augmented Lagrangian Value Function Method for Lower-level Constrained Stochastic Bilevel Optimization
by: Nie, Hantao, et al.
Published: (2025)

A Globalized Semismooth Newton Method for Prox-regular Optimization Problems
by: Wu, Yuqia, et al.
Published: (2025)

Semismooth Newton Augmented Lagrangian Algorithm for Adaptive Lasso Penalized Least Squares in Semiparametric Regression
by: Li, Peili, et al.
Published: (2021)

Oracle complexities of augmented Lagrangian methods for nonsmooth manifold optimization
by: Deng, Kangkang, et al.
Published: (2024)

Semismooth Newton Methods for Risk-Averse Markov Decision Processes
by: Gargiani, Matilde, et al.
Published: (2025)

Improving the communication in decentralized manifold optimization through single-step consensus and compression
by: Hu, Jiang, et al.
Published: (2024)

SLRQA: A Sparse Low-Rank Quaternion Model for Color Image Processing with Convergence Analysis
by: Deng, Zhanwang, et al.
Published: (2024)

Sparse Quadratically Constrained Quadratic Programming via Semismooth Newton Method
by: Li, Shuai, et al.
Published: (2025)

A Decentralized Primal-Dual Method with Quasi-Newton Tracking
by: Wang, Liping, et al.
Published: (2023)

LeAP-SSN: A Semismooth Newton Method with Global Convergence Rates
by: Alphonse, Amal, et al.
Published: (2025)

Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
by: Zhang, Penghe, et al.
Published: (2025)

A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport
by: Lin, Tianyi, et al.
Published: (2023)

Adaptive Riemannian ADMM for Nonsmooth Optimization: Optimal Complexity without Smoothing
by: Deng, Kangkang, et al.
Published: (2025)

Inertial Quasi-Newton Methods for Monotone Inclusion: Efficient Resolvent Calculus and Primal-Dual Methods
by: Wang, Shida, et al.
Published: (2022)

Skip the Hessian, Keep the Rates: Globalized Semismooth Newton with Lazy Hessian Updates
by: Alphonse, Amal, et al.
Published: (2026)

A Quasi-Newton Primal-Dual Algorithm with Line Search
by: Wang, Shida, et al.
Published: (2024)

Scalable Kernel Quantile Regression: A Preconditioned Augmented Lagrangian Method
by: Deng, Shengxiang, et al.
Published: (2025)

Two Typical Implementable Semismooth* Newton Methods for Generalized Equations are G-Semismooth Newton Methods
by: Chen, Liang, et al.
Published: (2024)

A Single-loop Stochastic Riemannian ADMM for Nonsmooth Optimization
by: Jin, Jiachen, et al.
Published: (2025)

A Universally Optimal Primal-Dual Method for Minimizing Heterogeneous Compositions
by: Zoll, Aaron, et al.
Published: (2025)

Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
by: Ke, Zhifa, et al.
Published: (2023)

Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
by: Ozaslan, Ibrahim K., et al.
Published: (2024)

Semismooth Newton Method for Boundary Bilinear Control
by: Casas, Eduardo, et al.
Published: (2024)

Smoothing Binary Optimization: A Primal-Dual Perspective
by: Liu, Wenbo, et al.
Published: (2025)

A Randomized Block-Coordinate Primal-Dual Method for Large-scale Stochastic Saddle Point Problems
by: Hamedani, Erfan Yazdandoost, et al.
Published: (2019)

A Bundle-based Augmented Lagrangian Framework: Algorithm, Convergence, and Primal-dual Principles
by: Liao, Feng-Yi, et al.
Published: (2025)

On the Differentiability of the Primal-Dual Interior-Point Method
by: Tracy, Kevin, et al.
Published: (2024)

Primal-Dual Strategy (PDS) for Composite Optimization Over Directed graphs
by: Zandi, Sajad, et al.
Published: (2024)

On the Oracle Complexity of a Riemannian Inexact Augmented Lagrangian Method for Riemannian Nonsmooth Composite Problems
by: Xu, Meng, et al.
Published: (2024)

When Does Primal Interior Point Method Beat Primal-dual in Linear Optimization?
by: Gao, Wenzhi, et al.
Published: (2024)

Two Innovations in Inexact Augmented Lagrangian Methods for Convex Optimization
by: Eckstein, Jonathan, et al.
Published: (2025)

Single-loop $\mathcal{O}(ε^{-3})$ stochastic smoothing algorithms for nonsmooth Riemannian optimization
by: Deng, Kangkang, et al.
Published: (2025)

Anderson acceleration of derivative-free projection methods for constrained monotone nonlinear equations
by: Jin, Jiachen, et al.
Published: (2024)

Quantum Alternating Direction Method of Multipliers for Semidefinite Programming
by: Nie, Hantao, et al.
Published: (2025)