Test3: Instance-to-Instance Comparison Results

Type: Instance
Submitter: MIPLIB submission pool
Description: Imported from the MIPLIB2010 submissions.
MIPLIB Entry

Parent Instance (Test3)

All other instances below were be compared against this "query" instance.

Test3 Raw Test3 Decomposed Test3 Composite of MIC top 5 Test3 Composite of MIPLIB top 5 Test3 Model Group Composite
Raw This is the CCM image before the decomposition procedure has been applied.
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.
Composite of MIC Top 5 Composite of the five decomposed CCM images from the MIC Top 5.
Composite of MIPLIB Top 5 Composite of the five decomposed CCM images from the MIPLIB Top 5.
Model Group Composite Image Composite of the decomposed CCM images for every instance in the same model group as this query.

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.
neos-3611447-jijia decomposed neos-3610040-iskar decomposed ns2124243 decomposed ns2122698 decomposed neos-3611689-kaihu decomposed
Name neos-3611447-jijia [MIPLIB] neos-3610040-iskar [MIPLIB] ns2124243 [MIPLIB] ns2122698 [MIPLIB] neos-3611689-kaihu [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.
1 / 0.945 2 / 0.950 3 / 0.968 4 / 0.985 5 / 0.991
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
neos-3611447-jijia raw neos-3610040-iskar raw ns2124243 raw ns2122698 raw neos-3611689-kaihu raw

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.
cbs-cta decomposed npmv07 decomposed neos-585192 decomposed neos-585467 decomposed minutedispatchstrategy decomposed
Name cbs-cta [MIPLIB] npmv07 [MIPLIB] neos-585192 [MIPLIB] neos-585467 [MIPLIB] minutedispatchstrategy [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.
19 / 1.085 362 / 1.434 482 / 1.531 510 / 1.555 630 / 1.691
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
cbs-cta raw npmv07 raw neos-585192 raw neos-585467 raw minutedispatchstrategy raw

Instance Summary

The table below contains summary information for Test3, the five most similar instances to Test3 according to the MIC, and the five most similar instances to Test3 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance Test3 [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 0.000000 -
MIC Top 5 neos-3611447-jijia [MIPLIB] Jeff Linderoth (None provided) 0.945180 1
neos-3610040-iskar [MIPLIB] Jeff Linderoth (None provided) 0.949588 2
ns2124243 [MIPLIB] Timo Berthold Instance coming from the NEOS Server with unknown application 0.967622 3
ns2122698 [MIPLIB] Timo Berthold Instance coming from the NEOS Server with unknown application. Solved by SCIP-CPLEX in 9500 seconds. 0.985267 4
neos-3611689-kaihu [MIPLIB] Jeff Linderoth (None provided) 0.991054 5
MIPLIB Top 5 cbs-cta [MIPLIB] Jordi Castro Set of MILP instances of the CTA (Controlled Tabular Adjustment) problem, a method to protect statistical tabular data, belonging to the field of SDC (Statistical Disclosure Control). Raw data of instances are real or pseudo-real, provided by several National Statistical Agencies. We generated the CTA problem for these data. 1.084973 19
npmv07 [MIPLIB] Q. Chen Unknown application 1.434306 362
neos-585192 [MIPLIB] NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.530724 482
neos-585467 [MIPLIB] NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.554788 510
minutedispatchstrategy [MIPLIB] Mark Husted Dispatch Strategy for a small micro-grid 1.691172 630


Test3: 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.
Model group: hypothyroid Model group: scp Model group: map Model group: supportvectormachine Model group: rmatr
Name hypothyroid scp map supportvectormachine rmatr
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.
1 / 1.448 2 / 1.644 3 / 1.648 4 / 1.664 5 / 1.679

Model Group Summary

The table below contains summary information for the five most similar model groups to Test3 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 hypothyroid Gleb Belov Linearized Constraint Programming models of the MiniZinc Challenges 2012-2016. I should be able to produce versions with indicator constraints supported by Gurobi and CPLEX, however don't know if you can use them and if there is a standard format. These MPS were produced by Gurobi 7.0.2 using the MiniZinc develop branch on eb536656062ca13325a96b5d0881742c7d0e3c38 1.447761 1
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 1.643694 2
map Kiyan Ahmadizadeh Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem 1.647862 3
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 1.663774 4
rmatr Dmitry Krushinsky Model coming from a formulation of the p-Median problem using square cost matrices 1.678696 5


Updated August 16, 2019