breastcancer-regularized

numerics variable_bound general_linear

Submitter	Variables	Constraints	Density	Status	Group	Objective	MPS File
Berk Ustun	715	723	1.6023e-02	easy	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 solved in 110s on 4 cores using Gurobi 9.0

Location of instance in collection

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.

Nonzero entries of original problem

Nonzero entries after trivial presolving

Decomposed structure of original problem (dec-file)

Decomposed structure after trivial presolving (dec-file)

	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.

## Warning in lapply(df["exactobjval"], as.numeric): NAs introduced by coercion

ID	Objective	Int. Viol	Submitter	Date	Description
3	35.76784	0.0e+00	Edward Rothberg	2019-12-18	Optimal solution found with Gurobi 9.0 in 110s on 4 cores
2	35.76784	0.0e+00	Hans Mittelmann	2019-11-15	Computed with Gurobi, added because of low integrality violation
1	35.71392	9.3e-06	-	2018-10-11	Solution found during MIPLIB2017 problem selection.

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	7022.953543477999*	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	hard	32674	32597	41	36	32709	417567	Berk Ustun	ustun	5642.121938895418	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}
}