Submitter Variables Constraints Density Status Group Objective MPS File
Berk Ustun 32674 32709 3.90712e-04 open ustun 7022.953543478 adult-regularized.mps.gz

MIP to create optimized data-driven scoring systems. See: https://github.com/ustunb/miplib2017-slim#miplib2017-slim for a description.

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 32674 32674
Constraints 32709 32709
Binaries 32597 32597
Integers 41 41
Continuous 36 36
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.000390712 0.000390712
Nonzeroes 417567 417567
Constraint Classification Properties
Original Presolved
Total 32709 32709
Empty 0 0
Free 0 0
Singleton 0 0
Aggregations 0 0
Precedence 0 0
Variable Bound 144 144
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 32565 32565
Indicator 0 0

Structure

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

value min median mean max
Components 1.568202
Constraint % 0.0122291 0.0122291 0.0122291 0.0122291
Variable % 0.0091800 0.0091800 0.0091800 0.0091800
Score 0.004402

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
1 7022.954 7022.954 0 0 0 - 2018-10-11 Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to adult-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 Variables Binaries Integers Continuous Constraints Nonz. Submitter Group Status Objective
breastcancer-regularized 715 692 14 9 723 8283 Berk Ustun ustun open 35.7139210526316*
adult-max5features 32674 32597 41 36 32709 417567 Berk Ustun ustun open 5848.097508813*
mushroom-best 8468 8237 118 113 8580 188735 Berk Ustun ustun easy 0.0553337612
neos-1456979 4605 4245 180 180 6770 36440 NEOS Server Submission neos-pseudoapplication-102 easy 176
supportcase33 20203 20102 101 0 20489 211915 Domenico Salvagnin easy -345

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 09, 2018 by Gregor Hendel
generated with R Markdown
© 2018 by Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB)
Imprint