A primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming

DOI10.1007/s10589-017-9930-9zbMath1394.90465OpenAlexW2739853462MaRDI QIDQ1694389

Peipei Tang, Cheng-Jing Wang

Publication date: 1 February 2018

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-017-9930-9

zbMATH Keywords

quadratic semidefinite programmingiteration complexitymajorized semismooth Newton-CG augmented Lagrangian method

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Methods of quasi-Newton type (90C53)

Related Items

On the Convergence Rate of Inexact Majorized sGS ADMM with Indefinite Proximal Terms for Convex Composite Programming

Uses Software

• QSDP
• SDPNAL+
• QSDPNAL

Cites Work

• Unnamed Item
• Unnamed Item
• On how to solve large-scale log-determinant optimization problems
• First-order methods of smooth convex optimization with inexact oracle
• An implementable proximal point algorithmic framework for nuclear norm minimization
• SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints
• An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming
• The rate of convergence of the augmented Lagrangian method for nonlinear semidefinite programming
• An inexact primal-dual path following algorithm for convex quadratic SDP
• Complementarity and nondegeneracy in semidefinite programming
• A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization
• Hankel Matrix Rank Minimization with Applications to System Identification and Realization
• An augmented Lagrangian dual approach for the H-weighted nearest correlation matrix problem
• A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
• Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm
• A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
• Monotone Operators and the Proximal Point Algorithm
• Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
• Variational Analysis
• Computational Complexity of Inexact Gradient Augmented Lagrangian Methods: Application to Constrained MPC
• An Accelerated Linearized Alternating Direction Method of Multipliers
• A SURVEY OF NUMERICAL METHODS FOR NONLINEAR SEMIDEFINITE PROGRAMMING
• Convex Analysis
• Iteration-complexity of first-order augmented Lagrangian methods for convex programming