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satellites4-25: Instance-to-Instance Comparison Results
| Type: | Instance |
| Submitter: | He Renjie |
| Description: | Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. |
| MIPLIB Entry |
Parent Instance (satellites4-25)
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 | satellites3-25 [MIPLIB] | satellites2-25 [MIPLIB] | sp98ar [MIPLIB] | sp97ar [MIPLIB] | piperout-d27 [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 / 0.612 | 2 / 0.820 | 3 / 0.862 | 4 / 0.914 | 5 / 1.051 | |
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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 | satellites3-25 [MIPLIB] | satellites2-25 [MIPLIB] | piperout-d20 [MIPLIB] | satellites2-60-fs [MIPLIB] | satellites2-40* [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 / 0.612 | 2 / 0.820 | 6 / 1.127 | 85 / 1.754 | 2* / 0.820* | |
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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 satellites4-25, the five most similar instances to satellites4-25 according to the MIC, and the five most similar instances to satellites4-25 according to MIPLIB 2017.
| INSTANCE | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| Parent Instance | satellites4-25 [MIPLIB] | He Renjie | Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. | 0.000000 | - |
| MIC Top 5 | satellites3-25 [MIPLIB] | He Renjie | Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. | 0.611963 | 1 |
| satellites2-25 [MIPLIB] | He Renjie | Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. | 0.819706 | 2 | |
| sp98ar [MIPLIB] | J. Goessens, S. v. Hoessel, L. Kroon | Railway line planning instance | 0.861618 | 3 | |
| sp97ar [MIPLIB] | J. Goessens, S. v. Hoessel, L. Kroon | Railway line planning instance. Solved with Gurobi 4.5.1 on a 12-core Linux system in 2678.77 sec. | 0.913757 | 4 | |
| piperout-d27 [MIPLIB] | Gleb Belov | Pipe routing with flexibility constraints. Instances with _GCM in the name are simple routing | 1.050710 | 5 | |
| MIPLIB Top 5 | satellites3-25 [MIPLIB] | He Renjie | Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. | 0.611963 | 1 |
| satellites2-25 [MIPLIB] | He Renjie | Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. | 0.819706 | 2 | |
| piperout-d20 [MIPLIB] | Gleb Belov | Pipe routing with flexibility constraints. Instances with _GCM in the name are simple routing | 1.126521 | 6 | |
| satellites2-60-fs [MIPLIB] | He Renjie | Satellite scheduling instance | 1.753864 | 85 | |
| satellites2-40* [MIPLIB] | He Renjie | The attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. | 0.819706* | 2* |
satellites4-25: Instance-to-Model Comparison Results
| Model Group Assignment from MIPLIB: | satellites |
| Assigned Model Group Rank/ISS in the MIC: | 1 / 1.318 |
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 | satellites | fhnw-sq | neos-pseudoapplication-70 | neos-pseudoapplication-108 | neos-pseudoapplication-1 | |
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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 / 1.318 | 2 / 1.407 | 3 / 1.561 | 4 / 1.636 | 5 / 1.640 |
Model Group Summary
The table below contains summary information for the five most similar model groups to satellites4-25 according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| MIC Top 5 | satellites | He Renjie | Ihe attachment is some models generated from real life satelliteschedule problem data,these models are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult models. | 1.318221 | 1 |
| fhnw-sq | Simon Felix | Combinatorial toy fesability problem: Magic square. Models 1 & 2 are feasible, model 3 is unknown. | 1.406820 | 2 | |
| neos-pseudoapplication-70 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 1.561262 | 3 | |
| neos-pseudoapplication-108 | NEOS Server Submission | Model coming from the NEOS Server with unknown application | 1.635688 | 4 | |
| neos-pseudoapplication-1 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 1.639681 | 5 |
