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neos-3610040-iskar: Instance-to-Instance Comparison Results
| Type: | Instance |
| Submitter: | Jeff Linderoth |
| Description: | (None provided) |
| MIPLIB Entry |
Parent Instance (neos-3610040-iskar)
All other instances below were be compared against this "query" instance.  |
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Raw
This is the CCM image before the decomposition procedure has been applied.
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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.
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Composite of MIC Top 5
Composite of the five decomposed CCM images from the MIC Top 5.
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Composite of MIPLIB Top 5
Composite of the five decomposed CCM images from the MIPLIB Top 5.
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Model Group Composite Image
Composite of the decomposed CCM images for every instance in the same model group as this query.
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MIC Top 5 Instances
These are the 5 decomposed CCM images that are most similar to decomposed CCM image for the the query instance, according to the ISS metric. |
Decomposed
These decomposed images were created by GCG.
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| Name | neos-3611447-jijia [MIPLIB] | neos-3611689-kaihu [MIPLIB] | neos-3610051-istra [MIPLIB] | icir97_potential [MIPLIB] | neos-3610173-itata [MIPLIB] | |
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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.
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1 / 0.332 | 2 / 0.382 | 3 / 0.734 | 4 / 0.740 | 5 / 0.744 | |
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Raw
These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
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MIPLIB Top 5 Instances
These are the 5 instances that are most closely related to the query instance, according to the instance statistic-based similarity measure employed by MIPLIB 2017 |
Decomposed
These decomposed images were created by GCG.
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| Name | neos-3611447-jijia [MIPLIB] | neos-3611689-kaihu [MIPLIB] | neos-3610051-istra [MIPLIB] | neos-3610173-itata [MIPLIB] | timtab1CUTS [MIPLIB] | |
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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.
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1 / 0.332 | 2 / 0.382 | 3 / 0.734 | 5 / 0.744 | 531 / 1.509 | |
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Raw
These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
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Instance Summary
The table below contains summary information for neos-3610040-iskar, the five most similar instances to neos-3610040-iskar according to the MIC, and the five most similar instances to neos-3610040-iskar according to MIPLIB 2017.
| INSTANCE | SUBMITTER | DESCRIPTION | ISS | RANK | |
|---|---|---|---|---|---|
| Parent Instance | neos-3610040-iskar [MIPLIB] | Jeff Linderoth | (None provided) | 0.000000 | - |
| MIC Top 5 | neos-3611447-jijia [MIPLIB] | Jeff Linderoth | (None provided) | 0.331549 | 1 |
| neos-3611689-kaihu [MIPLIB] | Jeff Linderoth | (None provided) | 0.382387 | 2 | |
| neos-3610051-istra [MIPLIB] | Jeff Linderoth | (None provided) | 0.734291 | 3 | |
| icir97_potential [MIPLIB] | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 0.740212 | 4 | |
| neos-3610173-itata [MIPLIB] | Jeff Linderoth | (None provided) | 0.743830 | 5 | |
| MIPLIB Top 5 | neos-3611447-jijia [MIPLIB] | Jeff Linderoth | (None provided) | 0.331549 | 1 |
| neos-3611689-kaihu [MIPLIB] | Jeff Linderoth | (None provided) | 0.382387 | 2 | |
| neos-3610051-istra [MIPLIB] | Jeff Linderoth | (None provided) | 0.734291 | 3 | |
| neos-3610173-itata [MIPLIB] | Jeff Linderoth | (None provided) | 0.743830 | 5 | |
| timtab1CUTS [MIPLIB] | C. Liebchen, R. Möhring | Public transport scheduling problem | 1.508940 | 531 |
neos-3610040-iskar: Instance-to-Model Comparison Results
| Model Group Assignment from MIPLIB: | neos-pseudoapplication-91 |
| Assigned Model Group Rank/ISS in the MIC: | 2 / 1.219 |
MIC Top 5 Model Groups
These are the 5 model group composite (MGC) images that are most similar to the decomposed CCM image for the query instance, according to the ISS metric. |
These are model group composite (MGC) images for the MIC top 5 model groups.
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| Name | hypothyroid | neos-pseudoapplication-91 | supportvectormachine | scp | map | |
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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.
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1 / 1.049 | 2 / 1.220 | 3 / 1.362 | 4 / 1.423 | 5 / 1.452 |
Model Group Summary
The table below contains summary information for the five most similar model groups to neos-3610040-iskar according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| MIC Top 5 | hypothyroid | 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 | 1.048874 | 1 |
| neos-pseudoapplication-91 | Jeff Linderoth | (None provided) | 1.219658 | 2 | |
| supportvectormachine | Toni Sorrell | Suport vector machine with ramp loss. GSVM2-RL is the formulation found in Hess E. and Brooks P. (2015) paper, The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization | 1.362002 | 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 | 1.422869 | 4 | |
| map | Kiyan Ahmadizadeh | Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem | 1.452441 | 5 |
