Computational Experience with Hypergraph-Based Methods for Automatic Decomposition in Discrete Optimization
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Publication:4922931
DOI10.1007/978-3-642-38171-3_31zbMath1382.90119OpenAlexW193699398MaRDI QIDQ4922931
Publication date: 4 June 2013
Published in: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-642-38171-3_31
Mixed integer programming (90C11) Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57) Hypergraphs (05C65) Combinatorial optimization (90C27)
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Consistency Cuts for Dantzig-Wolfe Reformulations ⋮ High-multiplicity \(N\)-fold IP via configuration LP ⋮ A data driven Dantzig-Wolfe decomposition framework ⋮ Computational aspects of column generation for nonlinear and conic optimization: classical and linearized schemes ⋮ Structure Detection in Mixed-Integer Programs ⋮ Learning when to use a decomposition ⋮ Random sampling and machine learning to understand good decompositions ⋮ Automatic Dantzig-Wolfe reformulation of mixed integer programs ⋮ Analysis of Sparse Cutting Planes for Sparse MILPs with Applications to Stochastic MILPs ⋮ A graph-based modeling abstraction for optimization: concepts and implementation in Plasmo.jl ⋮ Matrices of Optimal Tree-Depth and a Row-Invariant Parameterized Algorithm for Integer Programming
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