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neos-pseudoapplication-65
| Type: | Model Group |
| Submitter: | NEOS Server Submission |
| Description: | Imported from the MIPLIB2010 submissions. |
Parent Model Group (neos-pseudoapplication-65)
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 | neos4 |
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 | scp | neos-pseudoapplication-109 | markshare | stein | supportvectormachine | |
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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 / 1.674 | 2 / 1.772 | 3 / 1.816 | 4 / 1.850 | 5 / 1.892 |
Model Group Summary
The table below contains summary information for neos-pseudoapplication-65, and for the five most similar model groups to neos-pseudoapplication-65 according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| Parent Model Group | neos-pseudoapplication-65 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 0.000000 | - |
| MIC Top 5 | 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 | 1.673836 | 1 |
| neos-pseudoapplication-109 | Jeff Linderoth | (None provided) | 1.772484 | 2 | |
| markshare | G. Cornuéjols, M. Dawande | Market sharing problem | 1.815811 | 3 | |
| stein | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 1.850498 | 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 | 1.891695 | 5 |
