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neos-pseudoapplication-73
| Type: | Model Group |
| Submitter: | NEOS Server Submission |
| Description: | Imported from the MIPLIB2010 submissions. |
Parent Model Group (neos-pseudoapplication-73)
All other model groups below were be compared against this "query" model group.  |
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Model Group Composite (MGC) image
Composite of the decomposed CCM images for every instance in the query model group.
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Component Instances (Decomposed)
These are the decomposed CCM images for each instance in the query model group. |
These are component instance images.
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| Name | neos-662469 |
MIC Top 5 Model Groups
These are the 5 MGC images that are most similar to the MGC image for the query model group, according to the ISS metric. |
FIXME - These are model group composite images.
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| Name | exp_and_fc | neos-pseudoapplication-7 | noip | neos-pseudoapplication-99 | milo | |
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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 model group and all other model groups. A smaller ISS value indicates greater similarity.
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1 / 1.468 | 2 / 1.548 | 3 / 1.549 | 4 / 1.566 | 5 / 1.641 |
Model Group Summary
The table below contains summary information for neos-pseudoapplication-73, and for the five most similar model groups to neos-pseudoapplication-73 according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| Parent Model Group | neos-pseudoapplication-73 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 0.000000 | - |
| MIC Top 5 | exp_and_fc | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 1.467520 | 1 |
| neos-pseudoapplication-7 | Jeff Linderoth | (None provided) | 1.548348 | 2 | |
| noip | Christopher Hojny | integer programming formulation that verifies that no integer programming formulation of a given 0/1-point set exists | 1.548562 | 3 | |
| neos-pseudoapplication-99 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 1.565636 | 4 | |
| milo | Tamas Terlaky | The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 models, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. | 1.640703 | 5 |
