From feasibility to improvement to proof: three phases of solving mixed-integer programs

DOI10.1080/10556788.2017.1392519zbMath1398.90107OpenAlexW2766709291MaRDI QIDQ4637827

Gregor Hendel, Thorsten Koch, Timo Berthold

Publication date: 3 May 2018

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2017.1392519

zbMATH Keywords

branch-and-boundmixed integer programmingoptimization softwareadaptive search behaviouroptimality prediction

Mathematics Subject Classification ID

Mixed integer programming (90C11) Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57)

Related Items

Online learning for scheduling MIP heuristics

Uses Software

• SCIP
• SoPlex
• MIPLIB
• COIN-OR
• XPRESS
• ParaSCIP
• MIPLIB2003

Cites Work

• SCIP: solving constraint integer programs
• Progress in presolving for mixed integer programming
• Branching rules revisited
• Iterative-deepening search with on-line tree size prediction
• Faster MIP solutions via new node selection rules
• Measuring the impact of primal heuristics
• MIPLIB 2003
• Early Estimates of the Size of Branch-and-Bound Trees
• An Automatic Method of Solving Discrete Programming Problems
• Heuristic Sampling: A Method for Predicting the Performance of Tree Searching Programs
• Estimating the Efficiency of Backtrack Programs
• A Computational Study of Search Strategies for Mixed Integer Programming
• Rounding and Propagation Heuristics for Mixed Integer Programming
• Predicting the Performance of IDA* using Conditional Distributions
• Progress in Academic Computational Integer Programming
• A tree-search algorithm for mixed integer programming problems
• Experiments in mixed-integer linear programming
• Enhancing MIP Branching Decisions by Using the Sample Variance of Pseudo Costs