Mixed Integer Programming: Analyzing 12 Years of Progress

By Tobias Achterberg, Roland Wunderling. 2013

252 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Numerical Analysis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Andrea Lodi
2. Shabbir Ahmed
3. Maxime Gasse
4. Nicola Ferroni
5. Jayant R. Kalagnanam
6. Louis Wehenkel
7. George L. Nemhauser
8. Ignacio E. Grossmann

In Industrial and Manufacturing Engineering

1. Manuel Iori
2. Silvano Martello
3. Shabbir Ahmed
4. Andrea Lodi
5. Ignacio E. Grossmann
6. Maxence Delorme
7. Maxime Gasse
8. Nicola Ferroni

In Numerical Analysis

1. Jan Kronqvist
2. David E. Bernal
3. Andreas Lundell
4. George L. Nemhauser
5. Didier Chételat
6. Andrea Lodi
7. Jayant R. Kalagnanam
8. Louis Wehenkel

Citations in the corpus

• Learning to Run Heuristics in Tree Search (2017, background): “Even stronger results confirming the impact of heuristics on the solver CPLEX are discussed in [Achterberg and Wunderling, 2013].”
• Learning to Branch in Mixed Integer Programming (2016, background): “Variable selection for branching is considered to be a main component of modern MIP solvers (Linderoth and Savelsbergh 1999; Achterberg and Wunderling 2013).”
• Optimal Generalized Decision Trees via Integer Programming (2016, methodology): “As such, we can utilize significant advances in the power of modern MILP solvers [2, 1].”
• Bin packing and cutting stock problems: Mathematical models and exact algorithms (2016, background): “[1, 2) 138 133 137 136 138 138 138 138 138 138 138 127 69 [2, 3) 119 104 113 103 119 119 109 119 119 119 119 81 68 [3, 5) 251 154 199 144 249 251 141 251 251 221 251 96 104 [5, 10) 458 192 316 198 390 458 133 458 458 333 458 115 122 [10, n] 463 128 303 152 168 463 37 463 463 272 463 65 58”
• A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound (2014, background): “the process that divides the feasible region into two or more subproblems, is probably the key component that most conditions the efficiency of a solver [4].”
• Performance Variability in Mixed-Integer Programming (2013, methodology): “A deeper analysis of this topic is conducted by Achterberg and Wunderling [2].”
• A review and comparison of solvers for convex MINLP (2018, methodology): “This results in most of the solvers for MINLP relying on different MILP and NLP solvers.”
• Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2019, background): “A key step in the B&B algorithm is selecting a fractional variable to branch on in (2), which can have a very significant impact on the size of the resulting search tree [2].”