glass_q
POLIPlibrary for polynomially constrained
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glass_q| Name | glass_q |
| Classification | nc|bc|d2 |
| Problem type | maxfs |
| Description | Bilinear model for the MaxFS instance glass_q |
| Objective sense | min |
| Variables | 652 (214 binary, 0 general integer, 438 continuous) |
| Nonlinear variables | 438 |
| Constraints | 428 |
| Nonlinear constraints | 214 |
| Linear nonzeros | 2092 |
| Nonlinear nonzeros | 214 |
| Download | glass_q.pip.gz glass_q.gms.gz glass_q.mod.gz |
| Best known solution | |
| Best known objective | |
| Best known bound | |
| Originator | Marc Pfetsch |
| Formulator | Marc Pfetsch |
| Donator | Marc Pfetsch |
| References | AmaldiBruglieriCasale2008 Pfetsch2008 FrankAsuncion2010 |
| Links | The Maximum Feasible Subsystem problem Home page UCI Machine Learning Repository |
| Additional information | This is a bilinear (quadratic, complementary) model for the
complementary problem of the maximum feasible subsystem problem
(MaxFS). Given an infeasible linear inequality system, the MaxFS
problem consists of finding a subsystem of largest cardinality that is
feasible. The complementary problem removes the least number of
inequalities such that the resulting system is feasible.
The model introduces a slack variable for each linear inequality and a
binary variable that indicates whether a certain inequality is
fulfilled or not. For each inequality the corresponding two variables
are coupled via a complementary constraint, i.e., at least one of them
must be 0. This is a classical formulation. It has been used, for
instance, in Amaldi
et al. [2008]. A description of a branch-and-cut algorithm and
more information about the problem can be found in Pfetsch [2008].
The original instances can be accessed through the Maximum Feasible
Subsystem problem Home page. Some instances are based on data from the
UCI Machine Learning Repository.
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