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library for polynomially constrained
mixed-integer programming

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Open  breast-cancer-wisconsin_q

Namebreast-cancer-wisconsin_q
Classificationnc|bc|d2
Problem typemaxfs
DescriptionBilinear model for the MaxFS instance breast-cancer-wisconsin_q
Objective sensemin
Variables2059  (683 binary, 0 general integer, 1376 continuous)
Nonlinear variables1376
Constraints1366
Nonlinear constraints683
Linear nonzeros8879
Nonlinear nonzeros683
Download breast-cancer-wisconsin_q.pip.gz breast-cancer-wisconsin_q.gms.gz breast-cancer-wisconsin_q.mod.gz
Best known solution
Best known objective
Best known bound
OriginatorMarc Pfetsch
FormulatorMarc Pfetsch
DonatorMarc Pfetsch
References AmaldiBruglieriCasale2008 Pfetsch2008 FrankAsuncion2010
Links The Maximum Feasible Subsystem problem Home page UCI Machine Learning Repository
Additional informationThis 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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