Instance - bab1


	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	fastxgemm-n3r22s4t6 [MIPLIB]	fastxgemm-n3r23s5t6 [MIPLIB]	fastxgemm-n3r21s3t6 [MIPLIB]	graph20-20-1rand [MIPLIB]	mappingmesh3x3mpeg2i [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 / 0.789	2 / 0.799	3 / 0.824	4 / 0.919	5 / 0.928
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	cap6000 [MIPLIB]	nursesched-sprint02 [MIPLIB]	neos-933966 [MIPLIB]	mzzv42z [MIPLIB]	mzzv11 [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.	115 / 1.125	728 / 2.015	755 / 2.082	810 / 2.194	840 / 2.280
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 bab1, the five most similar instances to bab1 according to the MIC, and the five most similar instances to bab1 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	bab1 [MIPLIB]	Elmar Swarat, Laura Traverso, Julia Buwaya	Integrated vehicle routing and crew scheduling of toll inspectors on German highways	0.000000	-
MIC Top 5	fastxgemm-n3r22s4t6 [MIPLIB]	Laurent Sorber	Naive multiplication of two N by N matrices requires N^3 scalar multiplications. For N=2, Strassen showed that it could be done in only R=7 < 8=N^3 multiplications. For N=3, it is known that 19 <= R <= 23, and for N=4 it is known that 34 <= R <= 49. This repository contains code that generates a mixed-integer linear program (MILP) formulation of the fast matrix multiplication problem for finding solutions with R < N^3 and proving that they are optimal. For a more detailed description, see the accompanying manuscript.	0.789327	1
	fastxgemm-n3r23s5t6 [MIPLIB]	Laurent Sorber	Naive multiplication of two N by N matrices requires N^3 scalar multiplications. For N=2, Strassen showed that it could be done in only R=7 < 8=N^3 multiplications. For N=3, it is known that 19 <= R <= 23, and for N=4 it is known that 34 <= R <= 49. This repository contains code that generates a mixed-integer linear program (MILP) formulation of the fast matrix multiplication problem for finding solutions with R < N^3 and proving that they are optimal. For a more detailed description, see the accompanying manuscript.	0.798881	2
	fastxgemm-n3r21s3t6 [MIPLIB]	Laurent Sorber	Naive multiplication of two N by N matrices requires N^3 scalar multiplications. For N=2, Strassen showed that it could be done in only R=7 < 8=N^3 multiplications. For N=3, it is known that 19 <= R <= 23, and for N=4 it is known that 34 <= R <= 49. This repository contains code that generates a mixed-integer linear program (MILP) formulation of the fast matrix multiplication problem for finding solutions with R < N^3 and proving that they are optimal. For a more detailed description, see the accompanying manuscript.	0.824246	3
	graph20-20-1rand [MIPLIB]	Michael Bastubbe	Packing Cuts in Undirected Graphs. Instances are described in 4.1.	0.918513	4
	mappingmesh3x3mpeg2i [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	0.927573	5
MIPLIB Top 5	cap6000 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.124538	115
	nursesched-sprint02 [MIPLIB]	Haroldo Gambini Santos	Nurse Scheduling Problems from the First International Nurse Rostering Competition - INRC 2010	2.015137	728
	neos-933966 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	2.081514	755
	mzzv42z [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	2.193674	810
	mzzv11 [MIPLIB]	S. Lukac	Railway slot allocation problems	2.280083	840

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	mapping	graphs	nus-prxy	radiation	allcolor
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 / 1.327	2 / 1.386	3 / 1.419	4 / 1.423	5 / 1.427

Model Group Summary

The table below contains summary information for the five most similar model groups to bab1 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	mapping	Gleb Belov	These are the models 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 models can only be handled by solvers accepting indicator constraints. For models 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 models with full paths to mzn/dzn files of each model 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 model 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	1.327143	1
	graphs	Michael Bastubbe	Packing Cuts in Undirected Graphs. Models are described in 4.1.	1.385779	2
	nus-prxy	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.419003	3
	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	1.423301	4
	allcolor	Domenico Salvagnin	Prepack optimization model.	1.426828	5

Type:	Instance
Submitter:	Elmar Swarat, Laura Traverso, Julia Buwaya
Description:	Integrated vehicle routing and crew scheduling of toll inspectors on German highways
	MIPLIB Entry

Model Group Assignment from MIPLIB:	bab
Assigned Model Group Rank/ISS in the MIC:	219 / 3.938

bab1: Instance-to-Instance Comparison Results

Parent Instance (bab1)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

bab1: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary