Instance - van


	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	diameterc-mstc-v20a190d5i [MIPLIB]	ponderthis0517-inf [MIPLIB]	neos-2978205-isar [MIPLIB]	supportcase40 [MIPLIB]	bnatt400 [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 / 1.521	2 / 1.521	3 / 1.543	4 / 1.544	5 / 1.578
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	snip10x10-35r1budget17 [MIPLIB]	istanbul-no-cutoff [MIPLIB]	neos-3759587-noosa [MIPLIB]	neos-3755335-nizao [MIPLIB]	neos-5188808-nattai [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.	103 / 1.790	234 / 1.914	508 / 2.038	547 / 2.050	726 / 2.148
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 van, the five most similar instances to van according to the MIC, and the five most similar instances to van according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	van [MIPLIB]	C. Mannino, E. Parrello	Telecommunications network model	0.000000	-
MIC Top 5	diameterc-mstc-v20a190d5i [MIPLIB]	Gleb Belov	These are the instances from MiniZinc Challenges 2012-2016 (see www.minizinc.org), compiled for MIP WITH INDICATOR CONSTRAINTS using the develop branch of MiniZinc and CPLEX 12.7.1 on 30 April 2017. Thus, these instances can only be handled by solvers accepting indicator constraints. For instances compiled with big-M/domain decomposition only, see my previous submission to MIPLIB.To recompile, create a directory MODELS, a list lst12_16.txt of the instances with full paths to mzn/dzn files of each instance per line, and say$> ~/install/libmzn/tests/benchmarking/mzn-test.py -l ../lst12_16.txt -slvPrf MZN-CPLEX -debug 1 -addOption "-timeout 3 -D fIndConstr=true -D fMIPdomains=false" -useJoinedName "-writeModel MODELS_IND/%s.mps" Alternatively, you can compile individual instance as follows: $> mzn-cplex -v -s -G linear -output-time ../challenge_2012_2016/mznc2016_probs/zephyrus/zephyrus.mzn ../challenge_2012_2016/mznc2016_p/zephyrus/14__8__6__3.dzn -a -timeout 3 -D fIndConstr=true -D fMIPdomains=false -writeModel MODELS_IND/challenge_2012_2016mznc2016_probszephyruszephyrusmzn-challenge_2012_2016mznc2016_probszephyrus14__8__6__3dzn.mps	1.520597	1
	ponderthis0517-inf [MIPLIB]	Rob Pratt	Infeasible version of IBM Ponder This problem (with 52 characters and no spaces) from May 2017: https://www.research.ibm.com/haifa/ponderthis/challenges/May2017.html	1.521213	2
	neos-2978205-isar [MIPLIB]	Jeff Linderoth	(None provided)	1.542665	3
	supportcase40 [MIPLIB]	Domenico Salvagnin	Instance coming from IBM developerWorks forum with unknown application.	1.543971	4
	bnatt400 [MIPLIB]	Tatsuya Akutsu	Model to identify a singleton attractor in a Boolean network, applications in computational systems biology. Solved by SCIP 3.0 with SoPlex 1.7.0 in half an hour. A Intel Core2 Extreme CPU X9659 @3.00GHz was used.	1.577626	5
MIPLIB Top 5	snip10x10-35r1budget17 [MIPLIB]	Utz-Uwe Haus	Exact MILP reformulation using binary decision diagrams to obtain scenario bundles of 2-stage stochastic expected shortest path and expected maximum flow problem with decision dependent scenario probabilities. Notes: * very few binary variables * for each fixing of the binaries a system of equations computing conditioned probabilities remains	1.790185	103
	istanbul-no-cutoff [MIPLIB]	Utz-Uwe Haus	Exact MILP reformulation using binary decision diagrams to obtain scenario bundles of 2-stage stochastic expected shortest path and expected maximum flow problem with decision dependent scenario probabilities. Notes: * very few binary variables * for each fixing of the binaries a system of equations computing conditioned probabilities remains	1.913860	234
	neos-3759587-noosa [MIPLIB]	Jeff Linderoth	(None provided)	2.038254	508
	neos-3755335-nizao [MIPLIB]	Jeff Linderoth	(None provided)	2.050087	547
	neos-5188808-nattai [MIPLIB]	Jeff Linderoth	(None provided)	2.147836	726

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	bnatt	neos-pseudoapplication-90	neos-pseudoapplication-21	scp	neos-pseudoapplication-77
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 / 2.084	2 / 2.091	3 / 2.146	4 / 2.173	5 / 2.209

Model Group Summary

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

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	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.	2.084212	1
	neos-pseudoapplication-90	NEOS Server Submission	Model coming from the NEOS Server with unknown application	2.091083	2
	neos-pseudoapplication-21	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	2.145989	3
	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	2.172806	4
	neos-pseudoapplication-77	Jeff Linderoth	(None provided)	2.209156	5

Type:	Instance
Submitter:	C. Mannino, E. Parrello
Description:	Telecommunications network model
	MIPLIB Entry

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

van: Instance-to-Instance Comparison Results

Parent Instance (van)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

van: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary