Instance - snp-10-004-052


	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	snp-06-004-052 [MIPLIB]	snp-10-052-052 [MIPLIB]	neos-3611689-kaihu [MIPLIB]	hypothyroid-k1 [MIPLIB]	prod2 [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 / 0.329	2 / 0.665	3 / 1.251	4 / 1.253	5 / 1.256
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	snp-06-004-052 [MIPLIB]	snp-10-052-052 [MIPLIB]	snp-04-052-052 [MIPLIB]	snp-02-004-104 [MIPLIB]	cost266-UUE [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.	1 / 0.329	2 / 0.665	21 / 1.379	35 / 1.402	465 / 1.633
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 snp-10-004-052, the five most similar instances to snp-10-004-052 according to the MIC, and the five most similar instances to snp-10-004-052 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	snp-10-004-052 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	0.000000	-
MIC Top 5	snp-06-004-052 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	0.329321	1
	snp-10-052-052 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	0.664534	2
	neos-3611689-kaihu [MIPLIB]	Jeff Linderoth	(None provided)	1.250943	3
	hypothyroid-k1 [MIPLIB]	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	1.253176	4
	prod2 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.256109	5
MIPLIB Top 5	snp-06-004-052 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	0.329321	1
	snp-10-052-052 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	0.664534	2
	snp-04-052-052 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	1.378801	21
	snp-02-004-104 [MIPLIB]	Gerald Gamrath	Supply network planning problems.	1.402202	35
	cost266-UUE [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.633305	465

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	supplynetworkplanning	hypothyroid	scp	bnatt	supportvectormachine
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 / 1.597	2 / 1.771	3 / 1.835	4 / 1.931	5 / 1.933

Model Group Summary

The table below contains summary information for the five most similar model groups to snp-10-004-052 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	supplynetworkplanning	Gerald Gamrath	Supply network planning problems.	1.597134	1
	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	1.770853	2
	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	1.835481	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.	1.931441	4
	supportvectormachine	Toni Sorrell	Suport vector machine with ramp loss. GSVM2-RL is the formulation found in Hess E. and Brooks P. (2015) paper, The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization	1.933234	5

Type:	Instance
Submitter:	Gerald Gamrath
Description:	Supply network planning problems.
	MIPLIB Entry

snp-10-004-052: Instance-to-Instance Comparison Results

Parent Instance (snp-10-004-052)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

snp-10-004-052: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary

Model Group Assignment from MIPLIB:	supplynetworkplanning
Assigned Model Group Rank/ISS in the MIC:	1 / 1.597