Instance - noswot


	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	enlight8 [MIPLIB]	scpm1 [MIPLIB]	stein15inf [MIPLIB]	gen-ip016 [MIPLIB]	gen-ip002 [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 / 0.910	2 / 0.949	3 / 0.976	4 / 0.993	5 / 1.007
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	milo-v12-6-r1-58-1 [MIPLIB]	milo-v12-6-r1-75-1 [MIPLIB]	milo-v13-4-3d-4-0 [MIPLIB]	milo-v12-6-r2-40-1 [MIPLIB]	milo-v13-4-3d-3-0 [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.	108 / 1.246	118 / 1.257	249 / 1.333	269 / 1.344	425 / 1.440
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 noswot, the five most similar instances to noswot according to the MIC, and the five most similar instances to noswot according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	noswot [MIPLIB]	J. Gregory, L. Schrage	Unknown application	0.000000	-
MIC Top 5	enlight8 [MIPLIB]	A. Zymolka	Model to solve instance of a combinatorial game ``EnLight'' Imported from the MIPLIB2010 submissions.	0.910008	1
	scpm1 [MIPLIB]	Shunji Umetani	This is a random test instance 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 instances. We have also generated reduced instances 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 instance generator program from the following web site. https://sites.google.com/site/shunjiumetani/benchmark	0.948536	2
	stein15inf [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.976279	3
	gen-ip016 [MIPLIB]	Simon Bowly	Randomly generated integer and binary programming instances. These results are part of an early phase of work aimed at generating diverse and challenging MIP instances for experimental testing. We have aimed to produce small integer and binary programming instances which are reasonably difficult to solve and have varied structure, eliciting a range of behaviour in state of the art algorithms. Solved with XPRESS in a few seconds.	0.992632	4
	gen-ip002 [MIPLIB]	Simon Bowly	Randomly generated integer and binary programming instances. These results are part of an early phase of work aimed at generating diverse and challenging MIP instances for experimental testing. We have aimed to produce small integer and binary programming instances which are reasonably difficult to solve and have varied structure, eliciting a range of behaviour in state of the art algorithms.	1.007208	5
MIPLIB Top 5	milo-v12-6-r1-58-1 [MIPLIB]	Tamas Terlaky	The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time.	1.245852	108
	milo-v12-6-r1-75-1 [MIPLIB]	Tamas Terlaky	The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time.	1.257162	118
	milo-v13-4-3d-4-0 [MIPLIB]	Tamas Terlaky	The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time.	1.332550	249
	milo-v12-6-r2-40-1 [MIPLIB]	Tamas Terlaky	The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time.	1.344069	269
	milo-v13-4-3d-3-0 [MIPLIB]	Tamas Terlaky	The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time.	1.440239	425

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	scp	markshare	stein	neos-pseudoapplication-74	enlight
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 / 1.171	2 / 1.306	3 / 1.325	4 / 1.403	5 / 1.416

Model Group Summary

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

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	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	1.170790	1
	markshare	G. Cornuéjols, M. Dawande	Market sharing problem	1.305754	2
	stein	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.324643	3
	neos-pseudoapplication-74	Jeff Linderoth	(None provided)	1.402671	4
	enlight	A. Zymolka	Model to solve model of a combinatorial game ``EnLight'' Imported from the MIPLIB2010 submissions.	1.415854	5

Type:	Instance
Submitter:	J. Gregory, L. Schrage
Description:	Unknown application
	MIPLIB Entry

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

noswot: Instance-to-Instance Comparison Results

Parent Instance (noswot)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

noswot: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary