On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems
From MaRDI portal
Publication:6166652
DOI10.1007/s10589-023-00493-0arXiv2303.06893MaRDI QIDQ6166652
Xin Yee Lam, Kuang-Yu Ding, Kim-Chuan Toh
Publication date: 3 August 2023
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2303.06893
ADMMproximal augmented Lagrangian methodprogressive hedging algorithmsymmetric Gauss-Seidel decompositionconvex composite programmingdual block angular structure
Cites Work
- An augmented Lagrangian method for distributed optimization
- An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming
- A new polynomial-time algorithm for linear programming
- Partitioning procedures for solving mixed-variables programming problems
- Some advances in decomposition methods for stochastic linear programming
- A variable-penalty alternating directions method for convex optimization
- Interior-point methods with decomposition for solving large-scale linear programs
- A new interpretation of the progressive hedging algorithm for multistage stochastic minimization problems
- On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming
- A computational study of a solver system for processing two-stage stochastic LPs with enhanced Benders decomposition
- Parallel distributed-memory simplex for large-scale stochastic LP problems
- A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications
- Decomposition Based Interior Point Methods for Two-Stage Stochastic Convex Quadratic Programs with Recourse
- Self-concordance and Decomposition-based Interior Point Methods for the Two-stage Stochastic Convex Optimization Problem
- Scenarios and Policy Aggregation in Optimization Under Uncertainty
- A Primal-Dual Decomposition-Based Interior Point Approach to Two-Stage Stochastic Linear Programming
- Computing Block-Angular Karmarkar Projections with Applications to Stochastic Programming
- A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems
- On Convergence of an Augmented Lagrangian Decomposition Method for Sparse Convex Optimization
- On the Convergence Rate of Inexact Majorized sGS ADMM with Indefinite Proximal Terms for Convex Composite Programming
- Decomposition‐Based Interior Point Methods for Two‐Stage Stochastic Semidefinite Programming
- L-Shaped Linear Programs with Applications to Optimal Control and Stochastic Programming
- Upper Bounds, Secondary Constraints, and Block Triangularity in Linear Programming
- Multistage quadratic stochastic programming
- A log-barrier method with Benders decomposition for solving two-stage stochastic linear programs
