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neos-1442119: Instance-to-Instance Comparison Results
| Type: | Instance |
| Submitter: | NEOS Server Submission |
| Description: | Instance coming from the NEOS Server with unknown application |
| MIPLIB Entry |
Parent Instance (neos-1442119)
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-1430701 [MIPLIB] | chromaticindex32-8 [MIPLIB] | neos-5192052-neckar [MIPLIB] | neos-2991472-kalu [MIPLIB] | f2gap40400 [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 / 0.707 | 2 / 1.383 | 3 / 1.385 | 4 / 1.387 | 5 / 1.418 | |
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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 | neos-1430701 [MIPLIB] | neos-1171737 [MIPLIB] | neos-1171448 [MIPLIB] | neos-885086 [MIPLIB] | neos-1430701** [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 / 0.707 | 958 / 2.828 | 975 / 3.182 | 980 / 3.320 | N.A.** / N.A.** | |
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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 neos-1442119, the five most similar instances to neos-1442119 according to the MIC, and the five most similar instances to neos-1442119 according to MIPLIB 2017.
| INSTANCE | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| Parent Instance | neos-1442119 [MIPLIB] | NEOS Server Submission | Instance coming from the NEOS Server with unknown application | 0.000000 | - |
| MIC Top 5 | neos-1430701 [MIPLIB] | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 0.706990 | 1 |
| chromaticindex32-8 [MIPLIB] | Pierre Le Bodic | Simple edge-coloring model on chains of Petersen-like subgraphs, designed to fool MIP solvers into producing very large Branch-and-Bound trees. | 1.383231 | 2 | |
| neos-5192052-neckar [MIPLIB] | Jeff Linderoth | (None provided) | 1.385111 | 3 | |
| neos-2991472-kalu [MIPLIB] | Jeff Linderoth | (None provided) | 1.387008 | 4 | |
| f2gap40400 [MIPLIB] | Salim Haddadi | Restrictions of well-known hard generalized assignment problem instances (D10400,D20400,D40400,D15900,D30900,D60900,D201600,D401600,D801600) | 1.418453 | 5 | |
| MIPLIB Top 5 | neos-1430701 [MIPLIB] | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 0.706990 | 1 |
| neos-1171737 [MIPLIB] | NEOS Server Submission | Instance coming from the NEOS Server with unknown application | 2.827822 | 958 | |
| neos-1171448 [MIPLIB] | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 3.182335 | 975 | |
| neos-885086 [MIPLIB] | NEOS Server Submission | Instance coming from the NEOS Server with unknown application | 3.319741 | 980 | |
| neos-1430701** [MIPLIB] | NEOS Server Submission | Instance coming from the NEOS Server with unknown application. | N.A.** | N.A.** |
neos-1442119: Instance-to-Model Comparison Results
| Model Group Assignment from MIPLIB: | neos-pseudoapplication-8 |
| Assigned Model Group Rank/ISS in the MIC: | 230 / 4.135 |
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-77 | neos-pseudoapplication-74 | neos-pseudoapplication-40 | scp | bnatt | |
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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 / 1.953 | 2 / 1.981 | 3 / 1.985 | 4 / 2.065 | 5 / 2.068 |
Model Group Summary
The table below contains summary information for the five most similar model groups to neos-1442119 according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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| MIC Top 5 | neos-pseudoapplication-77 | Jeff Linderoth | (None provided) | 1.953451 | 1 |
| neos-pseudoapplication-74 | Jeff Linderoth | (None provided) | 1.981465 | 2 | |
| neos-pseudoapplication-40 | Jeff Linderoth | (None provided) | 1.985014 | 3 | |
| 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 | 2.064523 | 4 | |
| bnatt | Tatsuya Akutsu | We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an model of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009. | 2.067632 | 5 |
