Instance - neos-824661


	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-826224 [MIPLIB]	neos-5041756-cobark [MIPLIB]	radiationm18-12-05 [MIPLIB]	radiationm40-10-02 [MIPLIB]	neos-4408804-prosna [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 / 0.108	2 / 0.234	3 / 0.253	4 / 0.254	5 / 0.264
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	dws012-03 [MIPLIB]	dws008-03 [MIPLIB]	dws012-02 [MIPLIB]	dws012-01 [MIPLIB]	dws008-01 [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.	203 / 0.973	206 / 0.976	223 / 1.004	253 / 1.061	321 / 1.191
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-824661, the five most similar instances to neos-824661 according to the MIC, and the five most similar instances to neos-824661 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	neos-824661 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	0.000000	-
MIC Top 5	neos-826224 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	0.108074	1
	neos-5041756-cobark [MIPLIB]	Jeff Linderoth	(None provided)	0.233557	2
	radiationm18-12-05 [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	0.252663	3
	radiationm40-10-02 [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	0.253981	4
	neos-4408804-prosna [MIPLIB]	Hans Mittelmann	Collection of anonymous submissions to the NEOS Server for Optimization	0.263585	5
MIPLIB Top 5	dws012-03 [MIPLIB]	Philipp Leise	MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The instances vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions.	0.973449	203
	dws008-03 [MIPLIB]	Philipp Leise	MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The instances vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions.	0.975954	206
	dws012-02 [MIPLIB]	Philipp Leise	MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The instances vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions.	1.004092	223
	dws012-01 [MIPLIB]	Philipp Leise	MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The instances vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions.	1.061293	253
	dws008-01 [MIPLIB]	Philipp Leise	MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The instances vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions.	1.191369	321

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	n37	radiation	graphs	seqsolve	sp_product
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 / 0.299	2 / 0.395	3 / 0.597	4 / 0.658	5 / 0.661

Model Group Summary

The table below contains summary information for the five most similar model groups to neos-824661 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	n37	J. Aronson	Fixed charge transportation problem	0.298553	1
	radiation	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	0.394948	2
	graphs	Michael Bastubbe	Packing Cuts in Undirected Graphs. Models are described in 4.1.	0.597437	3
	seqsolve	Irv Lustig	The 3 problems in this group (seqsolve1-seqsolve3) represent a hierarchical optimization process, which is derived from a customer problem for assigning people to sites into blocks of time on days of the week. The specialty of this submission is that the best known solution for seqsolveX can be used as a MIP start for seqsolveX+1. For a description of the connections between the problems, please refer to the README.txt contained in the model data for this submission, which also includes MIP start files and a Gurobi log file.	0.657561	4
	sp_product	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.661340	5

Type:	Instance
Submitter:	NEOS Server Submission
Description:	Instance coming from the NEOS Server with unknown application
	MIPLIB Entry

Model Group Assignment from MIPLIB:	neos-pseudoapplication-93
Assigned Model Group Rank/ISS in the MIC:	121 / 2.624

neos-824661: Instance-to-Instance Comparison Results

Parent Instance (neos-824661)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

neos-824661: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary