Progress in Linear Programming-Based Algorithms for Integer Programming: An Exposition

By Ellis L. Johnson, George L. Nemhauser, Martin W. P. Savelsbergh. 2000

221 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering

How researchers see this paper

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

In Computer Networks and Communications

1. Greet Vanden Berghe
2. Matheus Guedes Vilas Boas
3. L. Merschmann
4. Haroldo Gambini Santos
5. Allison Chang
6. Ignacio E. Grossmann
7. Juan Pablo Vielma
8. Arnaud Fréville

In Industrial and Manufacturing Engineering

1. Haroldo Gambini Santos
2. Greet Vanden Berghe
3. Ignacio E. Grossmann
4. Juan Pablo Vielma
5. Arnaud Fréville
6. Matheus Guedes Vilas Boas
7. Jagat Patel
8. John W. Chinneck

Citations in the corpus

• Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, background): “Despite the continuous evolution of MIP solvers (Johnson et al., 2000; Gamrath et al., 2015), the optimization of large MIP models in restricted times, in the general case, is still challenging.”
• Active-constraint variable ordering for faster feasibility of mixed integer linear programs (2007, methodology): “[ 24 ]). The general steps of the method, summarized in Algorithm 1, are fairly standard, but there are numerous variations in the details.”
• An Annotated Bibliography of Personnel Scheduling and Rostering (2004, unknown): “, 1981), (Howick and Pidd, 1990), (Hung, 1997a), (Hung, 1995b), (Johnson et al., 1997), (Marsten and Shepardson, 1981), (Mehrotra, 1997), (Ramli et al.”
• Integer optimization methods for machine learning (2012, background): “1 In the 1980s, it was difficult to solve a problem with just a hundred integer variables, but now it is possible to solve problems with millions of integer variables [Johnson et al., 2000].”
• The multidimensional 0-1 knapsack problem: An overview (2004, methodology): “[100] mentioned that parallel branch-and-cut and parallel branch-and-price seem to have great potential in mixed integer programming.”
• Retrospective on optimization (2004, background): “See also Johnson et al. (2000) for a recent review on MILP.”
• Mixed Integer Linear Programming Formulation Techniques (2015, background): “For more details on basic branch-and-bound and state-of-the-art solvers, we refer the reader to [22, 36, 131, 166] and [1, 2, 25, 23, 92, 112, 24], respectively.”
• Review of Nonlinear Mixed-Integer and Disjunctive Programming Techniques (2002, background): “See also Johnson et al. (2000) for a recent review on MILP.”