rmatr100-p10: Instance-to-Instance Comparison Results

Type: Instance
Submitter: Dmitry Krushinsky
Description: Instance coming from a formulation of the p-Median problem using square cost matrices
MIPLIB Entry

Parent Instance (rmatr100-p10)

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

rmatr100-p10 Raw rmatr100-p10 Decomposed rmatr100-p10 Composite of MIC top 5 rmatr100-p10 Composite of MIPLIB top 5 rmatr100-p10 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.
rmatr100-p5 decomposed rmatr200-p5 decomposed neos-3209462-rhin decomposed neos-3046601-motu decomposed supportcase1 decomposed
Name rmatr100-p5 [MIPLIB] rmatr200-p5 [MIPLIB] neos-3209462-rhin [MIPLIB] neos-3046601-motu [MIPLIB] supportcase1 [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 / 0.175 2 / 0.445 3 / 0.450 4 / 0.456 5 / 0.459
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
rmatr100-p5 raw rmatr200-p5 raw neos-3209462-rhin raw neos-3046601-motu raw supportcase1 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.
rmatr100-p5 decomposed rmatr200-p5 decomposed rmatr200-p10 decomposed rmatr200-p20 decomposed neos-3665875-lesum decomposed
Name rmatr100-p5 [MIPLIB] rmatr200-p5 [MIPLIB] rmatr200-p10 [MIPLIB] rmatr200-p20 [MIPLIB] neos-3665875-lesum [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.
1 / 0.175 2 / 0.445 7 / 0.471 9 / 0.495 26 / 0.769
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
rmatr100-p5 raw rmatr200-p5 raw rmatr200-p10 raw rmatr200-p20 raw neos-3665875-lesum raw

Instance Summary

The table below contains summary information for rmatr100-p10, the five most similar instances to rmatr100-p10 according to the MIC, and the five most similar instances to rmatr100-p10 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance rmatr100-p10 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices 0.000000 -
MIC Top 5 rmatr100-p5 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices 0.174982 1
rmatr200-p5 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices 0.445120 2
neos-3209462-rhin [MIPLIB] Jeff Linderoth (None provided) 0.449722 3
neos-3046601-motu [MIPLIB] Jeff Linderoth (None provided) 0.456431 4
supportcase1 [MIPLIB] Michael Winkler MIP instances collected from Gurobi forum with unknown application 0.459156 5
MIPLIB Top 5 rmatr100-p5 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices 0.174982 1
rmatr200-p5 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices 0.445120 2
rmatr200-p10 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices. Solved by Gurobi 4.6.1 (12 threads) in 19644 seconds (January 2012). 0.471194 7
rmatr200-p20 [MIPLIB] Dmitry Krushinsky Instance coming from a formulation of the p-Median problem using square cost matrices. Solved using ug[SCIP/spx], a distributed massively parallel version of SCIP run on 2,000 cores at the HLRN-II super computer facility. 0.495464 9
neos-3665875-lesum [MIPLIB] Jeff Linderoth (None provided) 0.768807 26


rmatr100-p10: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: rmatr
Assigned Model Group Rank/ISS in the MIC: 1 / 0.369

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: rmatr Model group: map Model group: polygonpack Model group: neos-pseudoapplication-54 Model group: sp_product
Name rmatr map polygonpack neos-pseudoapplication-54 sp_product
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 / 0.369 2 / 0.520 3 / 0.795 4 / 0.851 5 / 1.006

Model Group Summary

The table below contains summary information for the five most similar model groups to rmatr100-p10 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 rmatr Dmitry Krushinsky Model coming from a formulation of the p-Median problem using square cost matrices 0.369239 1
map Kiyan Ahmadizadeh Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem 0.520088 2
polygonpack Antonio Frangioni Given a set P of polygons, not necessarily convex, and a rectangle, we want to find the subset S of P with largest possible total area and a position every p in S so that there are no overlaps and they are all included in the rectangle. We allow a small set of rotations (0, 90, 180, 270 degrees) for every polygon. The problem is simplified w.r.t. the real application because the polygons do not have (fully encircled) "holes", which are supposedly filled-in separately, although they can have "bays". Models are saved as .lp. Model LpPackingModel_Dim means that we are trying to pack polygons taken from set ; there are currently 5 different sets, and is 7, 10 or 15. 0.794954 3
neos-pseudoapplication-54 Jeff Linderoth (None provided) 0.850970 4
sp_product MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.005888 5


Updated August 16, 2019