sp97ar: Instance-to-Instance Comparison Results

Type: Instance
Submitter: J. Goessens, S. v. Hoessel, L. Kroon
Description: Railway line planning instance. Solved with Gurobi 4.5.1 on a 12-core Linux system in 2678.77 sec.
MIPLIB Entry

Parent Instance (sp97ar)

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

sp97ar Raw sp97ar Decomposed sp97ar Composite of MIC top 5 sp97ar Composite of MIPLIB top 5 sp97ar 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.
leo1 decomposed leo2 decomposed sp98ar decomposed satellites4-25 decomposed satellites3-25 decomposed
Name leo1 [MIPLIB] leo2 [MIPLIB] sp98ar [MIPLIB] satellites4-25 [MIPLIB] satellites3-25 [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 / 0.808 2 / 0.821 3 / 0.909 4 / 0.914 5 / 0.927
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
leo1 raw leo2 raw sp98ar raw satellites4-25 raw satellites3-25 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.
sp98ar decomposed sp98ic decomposed sp97ic decomposed n3div36 decomposed neos-1516309 decomposed
Name sp98ar [MIPLIB] sp98ic [MIPLIB] sp97ic [MIPLIB] n3div36 [MIPLIB] neos-1516309 [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.
3 / 0.909 7 / 0.960 32 / 1.383 475 / 2.198 772 / 2.384
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
sp98ar raw sp98ic raw sp97ic raw n3div36 raw neos-1516309 raw

Instance Summary

The table below contains summary information for sp97ar, the five most similar instances to sp97ar according to the MIC, and the five most similar instances to sp97ar according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance sp97ar [MIPLIB] J. Goessens, S. v. Hoessel, L. Kroon Railway line planning instance. Solved with Gurobi 4.5.1 on a 12-core Linux system in 2678.77 sec. 0.000000 -
MIC Top 5 leo1 [MIPLIB] COR@L test set Instance coming from the COR@L test set with unknown origin 0.808334 1
leo2 [MIPLIB] COR@L test set Instance coming from the COR@L test set with unknown origin 0.821293 2
sp98ar [MIPLIB] J. Goessens, S. v. Hoessel, L. Kroon Railway line planning instance 0.909485 3
satellites4-25 [MIPLIB] He Renjie Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. 0.913757 4
satellites3-25 [MIPLIB] He Renjie Ihe attachment is some instances generated from real life satelliteschedule problem data,these instances are easier comparable to real lifeproblem. The work is done by me and Alberto Ceselli from Univeristy ofMilano. I donnot know it is hard enough or not, if needs , I can generatemore difficult instances. 0.927459 5
MIPLIB Top 5 sp98ar [MIPLIB] J. Goessens, S. v. Hoessel, L. Kroon Railway line planning instance 0.909485 3
sp98ic [MIPLIB] J. Goessens, S. v. Hoessel, L. Kroon Railway line planning instance 0.960268 7
sp97ic [MIPLIB] J. Goessens, S. v. Hoessel, L. Kroon Railway line planning instance 1.382624 32
n3div36 [MIPLIB] R. Meirich Static line planning models on the Dutch IC network. Solved with Gurobi 4.5.1 on a 12-core Linux system in 1700.37 sec. 2.198019 475
neos-1516309 [MIPLIB] NEOS Server Submission Imported from the MIPLIB2010 submissions. 2.383905 772


sp97ar: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: sp9
Assigned Model Group Rank/ISS in the MIC: 96 / 2.831

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: neos-pseudoapplication-24 Model group: neos-pseudoapplication-104 Model group: chromaticindex Model group: neos-pseudoapplication-1 Model group: rail0
Name neos-pseudoapplication-24 neos-pseudoapplication-104 chromaticindex neos-pseudoapplication-1 rail0
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 / 1.504 2 / 1.524 3 / 1.558 4 / 1.628 5 / 1.700

Model Group Summary

The table below contains summary information for the five most similar model groups to sp97ar according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 neos-pseudoapplication-24 Jeff Linderoth (None provided) 1.503518 1
neos-pseudoapplication-104 Jeff Linderoth (None provided) 1.523656 2
chromaticindex 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. 1.558402 3
neos-pseudoapplication-1 NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.628233 4
rail0 Thomas Schlechte Track allocation problem modeled as arc coupling problem The problem was solved by CPLEX 12.4. It took approximately 170 hours. 1.700480 5