traininstance2

benchmark decomposition benchmark_suitable aggregations precedence variable_bound set_partitioning cardinality invariant_knapsack mixed_binary general_linear

Submitter	Variables	Constraints	Density	Status	Group	Objective	MPS File
Gleb Belov	12890	15603	2.06496e-04	easy	traininstance	71820	traininstance2.mps.gz

Linearized Constraint Programming models of the MiniZinc Challenges 2012-2016. I should be able to produce versions with indicator constraints supported by Gurobi and CPLEX, however don’t know if you can use them and if there is a standard format. These MPS were produced by Gurobi 7.0.2 using the MiniZinc develop branch on eb536656062ca13325a96b5d0881742c7d0e3c38. Solved with COPT 7.0 using up to 12 threads in less than 30 minutes.

Location of instance in collection

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
	Original	Presolved
Variables	12890	12853
Constraints	15603	15586
Binaries	5278	5247
Integers	2602	2596
Continuous	5010	5010
Implicit Integers	0	5
Fixed Variables	12	0
Nonzero Density	0.000206496	0.000206947
Nonzeroes	41531	41457

Constraint Classification Properties
	Original	Presolved
Total	15603	15586
Empty	0	0
Free	0	0
Singleton	0	0
Aggregations	10090	10100
Precedence	166	157
Variable Bound	35	44
Set Partitioning	22	10
Set Packing	0	0
Set Covering	0	0
Cardinality	17	16
Invariant Knapsack	17	16
Equation Knapsack	0	0
Bin Packing	0	0
Knapsack	2	0
Integer Knapsack	0	0
Mixed Binary	5000	5000
General Linear	254	243
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	1.633468
Constraint %		0.0064200	2.31313	0.0224561	19.2673
Variable %		0.0155473	2.35763	0.0466418	19.4729
Score	0.783913

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	71820	71820	0	0	0	-	2018-10-12	Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to traininstance2 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
traininstance6	easy	10218	4154	2056	4008	12309	32785	Gleb Belov	traininstance	28290	benchmark benchmark_suitable aggregations precedence variable_bound set_partitioning cardinality invariant_knapsack mixed_binary general_linear
neos-585467	easy	2116	846	0	1270	2166	50058	NEOS Server Submission	neos-pseudoapplication-92	399.3739	numerics aggregations precedence variable_bound mixed_binary
neos-585192	easy	2597	1044	0	1553	2628	72396	NEOS Server Submission	neos-pseudoapplication-92	461.1797	numerics aggregations precedence variable_bound mixed_binary
neos-4264598-oueme	open	54550	13370	0	41180	54714	191146	Jeff Linderoth	neos-pseudoapplication-57	6038453.676499*	numerics aggregations precedence variable_bound invariant_knapsack mixed_binary general_linear
radiationm40-10-02	hard	172013	62400	47213	62400	173603	406825	Gleb Belov	radiation	155328	benchmark decomposition benchmark_suitable aggregations precedence variable_bound integer_knapsack mixed_binary general_linear

Reference

@Inbook{Belov2016,
author="Belov, Gleb
and Stuckey, Peter J.
and Tack, Guido
and Wallace, Mark",
editor="Rueher, Michel",
title="Improved Linearization of Constraint Programming Models",
bookTitle="Principles and Practice of Constraint Programming: 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings",
year="2016",
publisher="Springer International Publishing",
pages="49--65",
isbn="978-3-319-44953-1",
doi="10.1007/978-3-319-44953-1_4",
url="http://dx.doi.org/10.1007/978-3-319-44953-1_4"
}