Instance - neos-3695882-vesdre


	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	neos-1456979 [MIPLIB]	neos-4954274-beardy [MIPLIB]	neos-1420546 [MIPLIB]	neos-1516309 [MIPLIB]	neos-3581454-haast [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 / 1.047	2 / 1.049	3 / 1.129	4 / 1.188	5 / 1.223
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	graphdraw-mainerd [MIPLIB]	graphdraw-opmanager [MIPLIB]	graphdraw-grafo2 [MIPLIB]	neos-3402294-bobin [MIPLIB]	neos-3135526-osun [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.	96 / 1.662	119 / 1.711	136 / 1.739	776 / 2.344	828 / 2.408
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 neos-3695882-vesdre, the five most similar instances to neos-3695882-vesdre according to the MIC, and the five most similar instances to neos-3695882-vesdre according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	neos-3695882-vesdre [MIPLIB]	Hans Mittelmann	Collection of anonymous submissions to the NEOS Server for Optimization	0.000000	-
MIC Top 5	neos-1456979 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.047342	1
	neos-4954274-beardy [MIPLIB]	Jeff Linderoth	Reported solved after 77000 seconds using Gurobi with 32 threads.	1.049492	2
	neos-1420546 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.129277	3
	neos-1516309 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.187805	4
	neos-3581454-haast [MIPLIB]	Jeff Linderoth	(None provided)	1.222918	5
MIPLIB Top 5	graphdraw-mainerd [MIPLIB]	Cézar Augusto Nascimento e Silva	In the Graph Drawing problem a set of symbols must be placed in a plane and their connections routed. The objective is to produce aesthetically pleasant, easy to read diagrams. As a primary concern one usually tries to minimize edges crossing, edges' length, waste of space and number of bents in the connections. When formulated with these constraints the problem becomes NP-Hard . In practice many additional complicating requirements can be included, such as non-uniform sizes for symbols. Thus, some heuristics such as the generalized force-direct method and Simulated Annealing have been proposed to tackle this problem. uses a grid structure to approach the Entity-Relationship (ER) drawing problem, emphasizing the differences between ER drawing and the more classical circuit drawing problems. presented different ways of producing graph layouts (e.g.: tree, orthogonal, visibility representations, hierarchic, among others) for general graphs with applications on different subjects. The ability to automatically produce high quality layouts is very important in many applications, one of these is Software Engineering: the availability of easy to understand ER diagrams, for instance, can improve the time needed for developers to master database models and increase their productivity. Our solution approach involves two phases: (\\(i\\)) firstly the optimal placement of entities is solved, i.e.: entities are positioned so as to minimize the distances between connected entities; and (\\(ii\\)) secondly, edges are routed minimizing bends and avoiding the inclusion of connectors too close. We present the model for the first phase of our problem.	1.661805	96
	graphdraw-opmanager [MIPLIB]	Cézar Augusto Nascimento e Silva	In the Graph Drawing problem a set of symbols must be placed in a plane and their connections routed. The objective is to produce aesthetically pleasant, easy to read diagrams. As a primary concern one usually tries to minimize edges crossing, edges' length, waste of space and number of bents in the connections. When formulated with these constraints the problem becomes NP-Hard . In practice many additional complicating requirements can be included, such as non-uniform sizes for symbols. Thus, some heuristics such as the generalized force-direct method and Simulated Annealing have been proposed to tackle this problem. uses a grid structure to approach the Entity-Relationship (ER) drawing problem, emphasizing the differences between ER drawing and the more classical circuit drawing problems. presented different ways of producing graph layouts (e.g.: tree, orthogonal, visibility representations, hierarchic, among others) for general graphs with applications on different subjects. The ability to automatically produce high quality layouts is very important in many applications, one of these is Software Engineering: the availability of easy to understand ER diagrams, for instance, can improve the time needed for developers to master database models and increase their productivity. Our solution approach involves two phases: (\\(i\\)) firstly the optimal placement of entities is solved, i.e.: entities are positioned so as to minimize the distances between connected entities; and (\\(ii\\)) secondly, edges are routed minimizing bends and avoiding the inclusion of connectors too close. We present the model for the first phase of our problem.	1.711437	119
	graphdraw-grafo2 [MIPLIB]	Cézar Augusto Nascimento e Silva	In the Graph Drawing problem a set of symbols must be placed in a plane and their connections routed. The objective is to produce aesthetically pleasant, easy to read diagrams. As a primary concern one usually tries to minimize edges crossing, edges' length, waste of space and number of bents in the connections. When formulated with these constraints the problem becomes NP-Hard . In practice many additional complicating requirements can be included, such as non-uniform sizes for symbols. Thus, some heuristics such as the generalized force-direct method and Simulated Annealing have been proposed to tackle this problem. uses a grid structure to approach the Entity-Relationship (ER) drawing problem, emphasizing the differences between ER drawing and the more classical circuit drawing problems. presented different ways of producing graph layouts (e.g.: tree, orthogonal, visibility representations, hierarchic, among others) for general graphs with applications on different subjects. The ability to automatically produce high quality layouts is very important in many applications, one of these is Software Engineering: the availability of easy to understand ER diagrams, for instance, can improve the time needed for developers to master database models and increase their productivity. Our solution approach involves two phases: (\\(i\\)) firstly the optimal placement of entities is solved, i.e.: entities are positioned so as to minimize the distances between connected entities; and (\\(ii\\)) secondly, edges are routed minimizing bends and avoiding the inclusion of connectors too close. We present the model for the first phase of our problem.	1.739092	136
	neos-3402294-bobin [MIPLIB]	Jeff Linderoth	(None provided)	2.344287	776
	neos-3135526-osun [MIPLIB]	Jeff Linderoth	Claimed infeasible by most solvers.	2.407513	828

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	neos-pseudoapplication-102	neos-pseudoapplication-9	sing	cvs	neos-pseudoapplication-14
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 / 2.082	2 / 2.093	3 / 2.157	4 / 2.163	5 / 2.192

Model Group Summary

The table below contains summary information for the five most similar model groups to neos-3695882-vesdre according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	neos-pseudoapplication-102	Hans Mittelmann	Seem to be VRP output from 2-hour runs of Gurobi on 12 threads is included	2.082268	1
	neos-pseudoapplication-9	NEOS Server Submission	Model coming from the NEOS Server with unknown application.	2.092833	2
	sing	Daniel Espinoza	Imported from the MIPLIB2010 submissions.	2.157168	3
	cvs	Michael Bastubbe	Capacitated vertex separator problem on randomly generated hypergraph with 128 vertices and 89 hyperedges in at most 16 components each including at most 8 vertices. solved with default GCG/Soplex in about 2000 seconds.	2.163261	4
	neos-pseudoapplication-14	Jeff Linderoth	(None provided)	2.191839	5

Type:	Instance
Submitter:	Hans Mittelmann
Description:	Collection of anonymous submissions to the NEOS Server for Optimization
	MIPLIB Entry

Model Group Assignment from MIPLIB:	neos-pseudoapplication-71
Assigned Model Group Rank/ISS in the MIC:	82 / 2.990

neos-3695882-vesdre: Instance-to-Instance Comparison Results

Parent Instance (neos-3695882-vesdre)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

neos-3695882-vesdre: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary