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neos-3230376-yser: Instance-to-Instance Comparison Results
Type: | Instance |
Submitter: | Jeff Linderoth |
Description: | (None provided) |
MIPLIB Entry |
Parent Instance (neos-3230376-yser)
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-3230516-zala [MIPLIB] | fhnw-binschedule0 [MIPLIB] | neos-3218348-suir [MIPLIB] | npmv07 [MIPLIB] | aligninq [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.
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1 / 0.713 | 2 / 0.876 | 3 / 0.899 | 4 / 1.009 | 5 / 1.074 | |
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-3230516-zala [MIPLIB] | genus-g61-25 [MIPLIB] | neos-3283608-agout [MIPLIB] | genus-sym-g62-2 [MIPLIB] | neos-3230516-zala** [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.
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1 / 0.713 | 224 / 1.461 | 429 / 1.608 | 829 / 2.171 | N.A.** / N.A.** | |
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-3230376-yser, the five most similar instances to neos-3230376-yser according to the MIC, and the five most similar instances to neos-3230376-yser according to MIPLIB 2017.
INSTANCE | SUBMITTER | DESCRIPTION | ISS | RANK | |
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Parent Instance | neos-3230376-yser [MIPLIB] | Jeff Linderoth | (None provided) | 0.000000 | - |
MIC Top 5 | neos-3230516-zala [MIPLIB] | Jeff Linderoth | (None provided) | 0.712567 | 1 |
fhnw-binschedule0 [MIPLIB] | Simon Felix | Scheduling/assignment for an industrial production pipeline | 0.876193 | 2 | |
neos-3218348-suir [MIPLIB] | Jeff Linderoth | (None provided) | 0.898972 | 3 | |
npmv07 [MIPLIB] | Q. Chen | Unknown application | 1.009057 | 4 | |
aligninq [MIPLIB] | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 1.074109 | 5 | |
MIPLIB Top 5 | neos-3230516-zala [MIPLIB] | Jeff Linderoth | (None provided) | 0.712567 | 1 |
genus-g61-25 [MIPLIB] | Stephan Beyer | Minimum Genus instance of g.61.25 (undirected) of the AT&T Graphs by Stephen C. North. | 1.461379 | 224 | |
neos-3283608-agout [MIPLIB] | Jeff Linderoth | (None provided) | 1.608344 | 429 | |
genus-sym-g62-2 [MIPLIB] | Stephan Beyer | Minimum Genus instance, with symmetry breaking constraints, of g.62.2 (undirected) of the AT&T Graphs by Stephen C. North. | 2.171199 | 829 | |
neos-3230516-zala** [MIPLIB] | Jeff Linderoth | (None provided) | N.A.** | N.A.** |
neos-3230376-yser: Instance-to-Model Comparison Results
Model Group Assignment from MIPLIB: | neos-pseudoapplication-4 |
Assigned Model Group Rank/ISS in the MIC: | 1 / 1.048 |
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 | neos-pseudoapplication-4 | neos-pseudoapplication-109 | scp | neos-pseudoapplication-74 | supportvectormachine | |
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 / 1.048 | 2 / 1.457 | 3 / 1.719 | 4 / 1.721 | 5 / 1.738 |
Model Group Summary
The table below contains summary information for the five most similar model groups to neos-3230376-yser according to the MIC.
MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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MIC Top 5 | neos-pseudoapplication-4 | Jeff Linderoth | (None provided) | 1.048443 | 1 |
neos-pseudoapplication-109 | Jeff Linderoth | (None provided) | 1.456785 | 2 | |
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 | 1.719312 | 3 | |
neos-pseudoapplication-74 | Jeff Linderoth | (None provided) | 1.720764 | 4 | |
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 | 1.737885 | 5 |