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ex1010-pi: Instance-to-Instance Comparison Results
Type: | Instance |
Submitter: | M. Winkler |
Description: | Logic synthesis problem from the 2010 SAT conference pseudo-Boolean competition |
MIPLIB Entry |
Parent Instance (ex1010-pi)
All other instances below were be compared against this "query" instance.
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 | qap10 [MIPLIB] | nw04 [MIPLIB] | air03 [MIPLIB] | neos-4382714-ruvuma [MIPLIB] | supportcase33 [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 / 1.248 | 2 / 1.392 | 3 / 1.463 | 4 / 1.476 | 5 / 1.514 | |
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 | glass-sc [MIPLIB] | iis-glass-cov [MIPLIB] | seymour [MIPLIB] | ramos3 [MIPLIB] | fast0507 [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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64 / 1.866 | 73 / 1.903 | 126 / 2.002 | 253 / 2.133 | 350 / 2.204 | |
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 ex1010-pi, the five most similar instances to ex1010-pi according to the MIC, and the five most similar instances to ex1010-pi according to MIPLIB 2017.
INSTANCE | SUBMITTER | DESCRIPTION | ISS | RANK | |
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Parent Instance | ex1010-pi [MIPLIB] | M. Winkler | Logic synthesis problem from the 2010 SAT conference pseudo-Boolean competition | 0.000000 | - |
MIC Top 5 | qap10 [MIPLIB] | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 1.248492 | 1 |
nw04 [MIPLIB] | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 1.392170 | 2 | |
air03 [MIPLIB] | G. Astfalk | Airline crew scheduling set partitioning problem | 1.462588 | 3 | |
neos-4382714-ruvuma [MIPLIB] | Hans Mittelmann | Collection of anonymous submissions to the NEOS Server for Optimization | 1.475902 | 4 | |
supportcase33 [MIPLIB] | Domenico Salvagnin | Instance coming from IBM developerWorks forum with unknown application. | 1.513608 | 5 | |
MIPLIB Top 5 | glass-sc [MIPLIB] | Marc Pfetsch | Set covering problems arising from a Benders algorithm for finding maximum feasible subsystems. More details on the generation is given in the README file in the tarball. | 1.866440 | 64 |
iis-glass-cov [MIPLIB] | Marc Pfetsch | 23 "middlehard" Set-Covering Instances for MIPLIB: they have a small number of variables compared to the number of constraints and CPLEX 12.1 needs about one hour to solve them.For more information, have a look into the readme file which explains how the instances can be created. | 1.902507 | 73 | |
seymour [MIPLIB] | W. Cook, P. Seymour | A set-covering problem that arose from work related to the proof of the 4-color theorem. | 2.002244 | 126 | |
ramos3 [MIPLIB] | F. Ramos | Set covering problem from a product manufacturing application | 2.132604 | 253 | |
fast0507 [MIPLIB] | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 2.204476 | 350 |
ex1010-pi: Instance-to-Model Comparison Results
Model Group Assignment from MIPLIB: | no model group assignment |
Assigned Model Group Rank/ISS in the MIC: | N.A. / N.A. |
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 | f2gap | nj | neos-pseudoapplication-101 | mod | generated | |
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 / 2.153 | 2 / 2.247 | 3 / 2.252 | 4 / 2.265 | 5 / 2.297 |
Model Group Summary
The table below contains summary information for the five most similar model groups to ex1010-pi according to the MIC.
MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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MIC Top 5 | f2gap | Salim Haddadi | Restrictions of well-known hard generalized assignment problem models (D10400,D20400,D40400,D15900,D30900,D60900,D201600,D401600,D801600) | 2.152617 | 1 |
nj | Jonathan Eckstein | Electoral district design with various levels of symmetry breaking constraints. | 2.246745 | 2 | |
neos-pseudoapplication-101 | NEOS Server Submission | Model coming from the NEOS Server with unknown application. Infeasibility claimed by CPLEX 12.6 and CPLEX 12.6.1 with extreme numerical caution emphasi after 4 and 2 hours computation, respectively. | 2.252442 | 3 | |
mod | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 2.264547 | 4 | |
generated | Simon Bowly | Randomly generated integer and binary programming models. These results are part of an early phase of work aimed at generating diverse and challenging MIP models for experimental testing. We have aimed to produce small integer and binary programming models which are reasonably difficult to solve and have varied structure, eliciting a range of behaviour in state of the art algorithms. | 2.296784 | 5 |