Dual Applications of Proximal Bundle Methods, Including Lagrangian Relaxation of Nonconvex Problems
From MaRDI portal
Publication:4509733
DOI10.1137/S1052623498332336zbMath0958.65070OpenAlexW1991716092MaRDI QIDQ4509733
Krzysztof C. Kiwiel, Stefan Feltenmark
Publication date: 19 October 2000
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/s1052623498332336
Lagrangian relaxationnumerical examplesconvex programmingnondifferentiable optimizationunit commitmentproximal bundle methodsconvexified relaxationspower production scheduling
Related Items
Decomposition algorithm for large-scale two-stage unit-commitment, Scalable branching on dual decomposition of stochastic mixed-integer programming problems, On decomposition and multiobjective-based column and disjunctive cut generation for MINLP, Inexact stabilized Benders' decomposition approaches with application to chance-constrained problems with finite support, A comparison of four approaches from stochastic programming for large-scale unit-commitment, The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming, Characterizing the solution set of convex optimization problems without convexity of constraints, On the computational efficiency of subgradient methods: a case study with Lagrangian bounds, Stochastic dual dynamic programming applied to nonconvex hydrothermal models, Large-scale unit commitment under uncertainty: an updated literature survey, Multi-Tree Decomposition Methods for Large-Scale Mixed Integer Nonlinear Optimization, Bundle methods for sum-functions with ``easy components: applications to multicommodity network design, Decomposition-based inner- and outer-refinement algorithms for global optimization, Duality gaps in nonconvex stochastic optimization, Incremental-like bundle methods with application to energy planning, Lagrangian smoothing heuristics for Max-cut, Divide to conquer: decomposition methods for energy optimization, An effective line search for the subgradient method, The omnipresence of Lagrange, A strongly convergent proximal bundle method for convex minimization in Hilbert spaces, Regularized decomposition of large scale block-structured robust optimization problems, On a primal-proximal heuristic in discrete optimization, A primal-proximal heuristic applied to the French unit-commitment problem, Revisiting augmented Lagrangian duals, Reduced subgradient bundle method for linearly constrained non-smooth non-convex problems, Large-scale unit commitment under uncertainty