On the computational efficiency of subgradient methods: a case study with Lagrangian bounds
From MaRDI portal
Publication:1697974
DOI10.1007/s12532-017-0120-7zbMath1393.90072OpenAlexW2611825068WikidataQ118165429 ScholiaQ118165429MaRDI QIDQ1697974
Antonio Frangioni, Bernard Gendron, Enrico Gorgone
Publication date: 21 February 2018
Published in: Mathematical Programming Computation (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/11568/851793
Lagrangian relaxationnondifferentiable optimizationsubgradient methodscomputational analysismulticommodity network design
Related Items
Lagrangian relaxation for SVM feature selection, A method for convex minimization based on translated first-order approximations, Lagrangian bounds for large‐scale multicommodity network design: a comparison between Volume and Bundle methods, Formulations and a Lagrangian relaxation approach for the prize collecting traveling salesman problem, Polyhedral results and stronger Lagrangean bounds for stable spanning trees, Incremental Bundle Methods using Upper Models, Dynamic smoothness parameter for fast gradient methods, A Lagrangian relaxation approach for stochastic network capacity expansion with budget constraints
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Primal-dual subgradient methods for convex problems
- Smooth minimization of non-smooth functions
- A stabilized structured Dantzig-Wolfe decomposition method
- Universal gradient methods for convex optimization problems
- Approximation accuracy, gradient methods, and error bound for structured convex optimization
- On embedding the volume algorithm in a variable target value method.
- Spectral projected subgradient with a momentum term for the Lagrangean dual approach
- A proximal cutting plane method using Chebychev center for nonsmooth convex optimization
- Two ``well-known properties of subgradient optimization
- On the choice of explicit stabilizing terms in column generation
- Conditional subgradient optimization -- theory and applications
- On improvements to the analytic center cutting plane method
- Error stability properties of generalized gradient-type algorithms
- Efficient cuts in Lagrangean `relax-and-cut' schemes
- Convergence of a simple subgradient level method
- Multicommodity network flows: The impact of formulation on decomposition
- The volume algorithm revisited: relation with bundle methods
- The volume algorithm: Producing primal solutions with a subgradient method
- A generalized subgradient method with relaxation step
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Ergodic, primal convergence in dual subgradient schemes for convex programming
- A variable target value method for nondifferentiable optimization
- Bundle methods for sum-functions with ``easy components: applications to multicommodity network design
- Projected subgradient minimization versus superiorization
- New developments in the primal-dual column generation technique
- A variable smoothing algorithm for solving convex optimization problems
- A library for continuous convex separable quadratic knapsack problems
- Comparison of bundle and classical column generation
- Solving semidefinite quadratic problems within nonsmooth optimization algorithms
- New approaches for optimizing over the semimetric polytope
- The Efficiency of Ballstep Subgradient Level Methods for Convex Optimization
- Incremental Subgradient Methods for Nondifferentiable Optimization
- A Simple but Usually Fast Branch-and-Bound Algorithm for the Capacitated Facility Location Problem
- A family of subgradient-based methods for convex optimization problems in a unifying framework
- A Nonmonotone Proximal Bundle Method with (Potentially) Continuous Step Decisions
- Symmetric and Asymmetric Parallelization of a Cost-Decomposition Algorithm for Multicommodity Flow Problems
- Smoothing and First Order Methods: A Unified Framework
- The Cutting-Plane Method for Solving Convex Programs
- Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods
- A Bundle Type Dual-Ascent Approach to Linear Multicommodity Min-Cost Flow Problems
- Dual Applications of Proximal Bundle Methods, Including Lagrangian Relaxation of Nonconvex Problems
- Convergence of Approximate and Incremental Subgradient Methods for Convex Optimization
- Generalized Bundle Methods
- Application of a Smoothing Technique to Decomposition in Convex Optimization
- Convergence Analysis of Deflected Conditional Approximate Subgradient Methods
- Excessive Gap Technique in Nonsmooth Convex Minimization
- The Traveling-Salesman Problem and Minimum Spanning Trees
- Minimization of unsmooth functionals
- A modified subgradient algorithm for Lagrangean relaxation
- A geometric study of dual gaps, with applications
- Bundle-based relaxation methods for multicommodity capacitated fixed charge network design
- Bundle methods in stochastic optimal power management: A disaggregated approach using preconditioners