Instance - prod1


	Raw This is the CCM image before the decomposition procedure has been applied.	Decomposed This is the CCM image after a decomposition procedure has been applied. This is the image used by the MIC's image-based comparisons for this query instance.	Composite of MIC Top 5 Composite of the five decomposed CCM images from the MIC Top 5.	Composite of MIPLIB Top 5 Composite of the five decomposed CCM images from the MIPLIB Top 5.	Model Group Composite Image Composite of the decomposed CCM images for every instance in the same model group as this query.

Decomposed These decomposed images were created by GCG.
Name	8div-n59k10 [MIPLIB]	8div-n59k12 [MIPLIB]	8div-n59k11 [MIPLIB]	prod2 [MIPLIB]	gt2 [MIPLIB]
Rank / ISS The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query instance and all other instances. A smaller ISS value indicates greater similarity.	1 / 0.653	2 / 0.658	3 / 0.663	4 / 0.754	5 / 0.760
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.

Decomposed These decomposed images were created by GCG.
Name	prod2 [MIPLIB]	neos-1430701 [MIPLIB]	neos-1442119 [MIPLIB]	ns1208400 [MIPLIB]	neos-1171448 [MIPLIB]
Rank / ISS The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query instance and all model groups. A smaller ISS value indicates greater similarity.	4 / 0.754	76 / 1.093	564 / 1.535	763 / 1.858	961 / 3.153
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.

Instance Summary

The table below contains summary information for prod1, the five most similar instances to prod1 according to the MIC, and the five most similar instances to prod1 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	prod1 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.000000	-
MIC Top 5	8div-n59k10 [MIPLIB]	Sascha Kurz	Projective binary 8-divisible linear block codes A linear block code is called 8-divisible if the weights of its codewords are divisible by 8. It is called projective if there are no duplicate columns in the generator matrix. The possible lengths of 8-divisible linear block codes have been classified except for length n=59, where it is undecided whether such a linear code exists. The possible dimensions satisfy \\(10 \\le k \\le 20\\). Instance 8div_n59_kXX contains the corresponding feasibility problem. Projective binary 8-divisible linear block codes occur as hole configurations of so-called partial solid spreads in finite geometry. Binary 4-divisible linear block codes have applications in physics.	0.653025	1
	8div-n59k12 [MIPLIB]	Sascha Kurz	Projective binary 8-divisible linear block codes A linear block code is called 8-divisible if the weights of its codewords are divisible by 8. It is called projective if there are no duplicate columns in the generator matrix. The possible lengths of 8-divisible linear block codes have been classified except for length n=59, where it is undecided whether such a linear code exists. The possible dimensions satisfy \\(10 \\le k \\le 20\\). Instance 8div_n59_kXX contains the corresponding feasibility problem. Projective binary 8-divisible linear block codes occur as hole configurations of so-called partial solid spreads in finite geometry. Binary 4-divisible linear block codes have applications in physics.	0.658315	2
	8div-n59k11 [MIPLIB]	Sascha Kurz	Projective binary 8-divisible linear block codes A linear block code is called 8-divisible if the weights of its codewords are divisible by 8. It is called projective if there are no duplicate columns in the generator matrix. The possible lengths of 8-divisible linear block codes have been classified except for length n=59, where it is undecided whether such a linear code exists. The possible dimensions satisfy \\(10 \\le k \\le 20\\). Instance 8div_n59_kXX contains the corresponding feasibility problem. Projective binary 8-divisible linear block codes occur as hole configurations of so-called partial solid spreads in finite geometry. Binary 4-divisible linear block codes have applications in physics.	0.662822	3
	prod2 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.754176	4
	gt2 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.759981	5
MIPLIB Top 5	prod2 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.754176	4
	neos-1430701 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.092954	76
	neos-1442119 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	1.534657	564
	ns1208400 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	1.858146	763
	neos-1171448 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	3.152592	961

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	neos-pseudoapplication-74	8div	hypothyroid	bnatt	scp
Rank / ISS The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query instance and all other instances. A smaller ISS value indicates greater similarity.	1 / 1.447	2 / 1.493	3 / 1.522	4 / 1.622	5 / 1.638

Model Group Summary

The table below contains summary information for the five most similar model groups to prod1 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	neos-pseudoapplication-74	Jeff Linderoth	(None provided)	1.446882	1
	8div	Sascha Kurz	Projective binary 8-divisible linear block codes A linear block code is called 8-divisible if the weights of its codewords are divisible by 8. It is called projective if there are no duplicate columns in the generator matrix. The possible lengths of 8-divisible linear block codes have been classified except for length n=59, where it is undecided whether such a linear code exists. The possible dimensions satisfy \\(10 \\le k \\le 20\\). Model 8div_n59_kXX contains the corresponding feasibility problem. Projective binary 8-divisible linear block codes occur as hole configurations of so-called partial solid spreads in finite geometry. Binary 4-divisible linear block codes have applications in physics.	1.493220	2
	hypothyroid	Gleb Belov	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	1.521888	3
	bnatt	Tatsuya Akutsu	We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an model of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009.	1.622448	4
	scp	Shunji Umetani	This is a random test model generator for SCP using the scheme of the following paper, namely the column cost c[j] are integer randomly generated from [1,100]; every column covers at least one row; and every row is covered by at least two columns. see reference: E. Balas and A. Ho, Set covering algorithms using cutting planes, heuristics, and subgradient optimization: A computational study, Mathematical Programming, 12 (1980), 37-60. We have newly generated Classes I-N with the following parameter values, where each class has five models. We have also generated reduced models by a standard pricing method in the following paper: S. Umetani and M. Yagiura, Relaxation heuristics for the set covering problem, Journal of the Operations Research Society of Japan, 50 (2007), 350-375. You can obtain the model generator program from the following web site. https://sites.google.com/site/shunjiumetani/benchmark	1.637504	5

Type:	Instance
Submitter:	MIPLIB submission pool
Description:	Imported from the MIPLIB2010 submissions.
	MIPLIB Entry

Model Group Assignment from MIPLIB:	no model group assignment
Assigned Model Group Rank/ISS in the MIC:	N.A. / N.A.

prod1: Instance-to-Instance Comparison Results

Parent Instance (prod1)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

prod1: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary