Random sampling and machine learning to understand good decompositions
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Publication:2288981
DOI10.1007/s10479-018-3067-9zbMath1497.90136OpenAlexW2894407851MaRDI QIDQ2288981
Alberto Ceselli, Andrea G. B. Tettamanzi, Saverio Basso
Publication date: 20 January 2020
Published in: Annals of Operations Research (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/2434/487931
Related Items (5)
Dantzig-Wolfe reformulations for binary quadratic problems ⋮ A data driven Dantzig-Wolfe decomposition framework ⋮ Adaptive solution prediction for combinatorial optimization ⋮ Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems ⋮ Comments on: ``On learning and branching: a survey
Uses Software
Cites Work
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- Unnamed Item
- Bin packing and cutting stock problems: mathematical models and exact algorithms
- Algorithm runtime prediction: methods \& evaluation
- A branch-and-price algorithm for the variable size bin packing problem with minimum filling constraint
- SCIP: solving constraint integer programs
- Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model
- A computational evaluation of a general branch-and-price framework for capacitated network location problems
- Breakthroughs in statistics. Volume I: Foundations and basic theory
- Dantzig-Wolfe decomposition and branch-and-price solving in G12
- MIPLIB 2003
- Automatic Dantzig-Wolfe reformulation of mixed integer programs
- Reformulation and Decomposition of Integer Programs
- Computational Experience with Hypergraph-Based Methods for Automatic Decomposition in Discrete Optimization
- Asynchronous Column Generation
- Column Generation
- Probability and Computing
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