breastcancer-regularized

numerics variable_bound general_linear

Submitter Variables Constraints Density Status Group Objective MPS File
Berk Ustun 715 723 1.6023e-02 hard ustun 35.76784210526315 breastcancer-regularized.mps.gz

MIP to create optimized data-driven scoring systems. See: https://github.com/ustunb/miplib2017-slim#miplib2017-slim for a description. Solution was computed with Gurobi, chosen because of low integrality violation

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 715 715
Constraints 723 723
Binaries 692 692
Integers 14 14
Continuous 9 9
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.016023 0.016023
Nonzeroes 8283 8283
Constraint Classification Properties
Original Presolved
Total 723 723
Empty 0 0
Free 0 0
Singleton 0 0
Aggregations 0 0
Precedence 0 0
Variable Bound 36 36
Set Partitioning 0 0
Set Packing 0 0
Set Covering 0 0
Cardinality 0 0
Invariant Knapsack 0 0
Equation Knapsack 0 0
Bin Packing 0 0
Knapsack 0 0
Integer Knapsack 0 0
Mixed Binary 0 0
General Linear 687 687
Indicator 0 0

Structure

Available nonzero structure and decomposition information. Further information can be found here.

value min median mean max
Components 1.041393
Constraint % 0.276625 0.525588 0.55325 0.55325
Variable % 0.419580 3.762240 0.41958 33.84620
Score 0.051414

Best Known Solution(s)

Find solutions below. Download the archive containing all solutions from the Download page.

ID Objective Exact Int. Viol Cons. Viol Obj. Viol Submitter Date Description
2 1 35.71392 9.3e-06 0 0 - 2018-10-11 Solution found during MIPLIB2017 problem selection.
1 2 35.76784 0.0e+00 0 0 Hans Mittelmann 2019-11-15 Computed with Gurobi, added because of low integrality violation

Similar instances in collection

The following instances are most similar to breastcancer-regularized in the collection. This similarity analysis is based on 100 scaled instance features describing properties of the variables, objective function, bounds, constraints, and right hand sides.

Instance Status Variables Binaries Integers Continuous Constraints Nonz. Submitter Group Objective Tags
adult-regularized open 32674 32597 41 36 32709 417567 Berk Ustun ustun 7022.953543478* variable_bound general_linear
mushroom-best easy 8468 8237 118 113 8580 188735 Berk Ustun ustun 0.0553337612 benchmark benchmark_suitable variable_bound general_linear
adult-max5features open 32674 32597 41 36 32709 417567 Berk Ustun ustun 5848.097508813* variable_bound general_linear
lectsched-4-obj easy 7901 7665 236 0 14163 82428 Harald Schilly lectsched 4 benchmark_suitable aggregations precedence variable_bound set_covering integer_knapsack general_linear
lectsched-5-obj easy 21805 21389 416 0 38884 239608 Harald Schilly lectsched 24 benchmark benchmark_suitable aggregations precedence variable_bound set_covering invariant_knapsack integer_knapsack general_linear

Reference

@article{
    ustun2015slim,
    year = {2015},
    issn = {0885-6125},
    journal = {Machine Learning},
    doi = {10.1007/s10994-015-5528-6},
    title = {Supersparse linear integer models for optimized medical scoring systems},
    url = {http://dx.doi.org/10.1007/s10994-015-5528-6},
    publisher = { Springer US},
    author = {Ustun, Berk and Rudin, Cynthia},
    pages = {1-43},
    language = {English}
}

Last Update Nov 22, 2019 by Gregor Hendel
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