neos17: Instance-to-Instance Comparison Results

Type: Instance
Submitter: NEOS Server Submission
Description: Imported from the MIPLIB2010 submissions.
MIPLIB Entry

Parent Instance (neos17)

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

neos17 Raw neos17 Decomposed neos17 Composite of MIC top 5 neos17 Composite of MIPLIB top 5 neos17 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.
vpphard decomposed vpphard2 decomposed neos-4322846-ryton decomposed graph40-40-1rand decomposed graph40-80-1rand decomposed
Name vpphard [MIPLIB] vpphard2 [MIPLIB] neos-4322846-ryton [MIPLIB] graph40-40-1rand [MIPLIB] graph40-80-1rand [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 / 0.838 2 / 0.840 3 / 1.056 4 / 1.059 5 / 1.064
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
vpphard raw vpphard2 raw neos-4322846-ryton raw graph40-40-1rand raw graph40-80-1rand 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.
neos-3209519-ruhr decomposed neos-3610173-itata decomposed neos-3611447-jijia decomposed neos-3610040-iskar decomposed neos-3611689-kaihu decomposed
Name neos-3209519-ruhr [MIPLIB] neos-3610173-itata [MIPLIB] neos-3611447-jijia [MIPLIB] neos-3610040-iskar [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 model groups. A smaller ISS value indicates greater similarity.
111 / 1.227 236 / 1.363 305 / 1.437 314 / 1.449 336 / 1.472
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
neos-3209519-ruhr raw neos-3610173-itata raw neos-3611447-jijia raw neos-3610040-iskar raw neos-3611689-kaihu raw

Instance Summary

The table below contains summary information for neos17, the five most similar instances to neos17 according to the MIC, and the five most similar instances to neos17 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance neos17 [MIPLIB] NEOS Server Submission Imported from the MIPLIB2010 submissions. 0.000000 -
MIC Top 5 vpphard [MIPLIB] C. Cardonha Vehicle positioning problem instance 0.838082 1
vpphard2 [MIPLIB] C. Cardonha Vehicle positioning problem instance. Solved using CPLEX 12.4 in 43987 seconds (May 2012). Solved using Gurobi 5.6.2 in 124 seconds (May 2014).Solved using CPLEX 12.6 in 225 seconds (May 2014). 0.839980 2
neos-4322846-ryton [MIPLIB] Jeff Linderoth (None provided) 1.056306 3
graph40-40-1rand [MIPLIB] Michael Bastubbe Packing Cuts in Undirected Graphs. Instances are described in 4.1. 1.058983 4
graph40-80-1rand [MIPLIB] Michael Bastubbe Packing Cuts in Undirected Graphs. Instances are described in 4.1. 1.063810 5
MIPLIB Top 5 neos-3209519-ruhr [MIPLIB] Jeff Linderoth (None provided) 1.227260 111
neos-3610173-itata [MIPLIB] Jeff Linderoth (None provided) 1.363257 236
neos-3611447-jijia [MIPLIB] Jeff Linderoth (None provided) 1.437292 305
neos-3610040-iskar [MIPLIB] Jeff Linderoth (None provided) 1.448716 314
neos-3611689-kaihu [MIPLIB] Jeff Linderoth (None provided) 1.472438 336


neos17: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: neos-pseudoapplication-66
Assigned Model Group Rank/ISS in the MIC: 136 / 2.927

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: map Model group: sp_product Model group: rmatr Model group: neos-pseudoapplication-2 Model group: polygonpack
Name map sp_product rmatr neos-pseudoapplication-2 polygonpack
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 / 1.294 2 / 1.348 3 / 1.363 4 / 1.366 5 / 1.442

Model Group Summary

The table below contains summary information for the five most similar model groups to neos17 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 map Kiyan Ahmadizadeh Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem 1.294310 1
sp_product MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.348419 2
rmatr Dmitry Krushinsky Model coming from a formulation of the p-Median problem using square cost matrices 1.362792 3
neos-pseudoapplication-2 NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.366226 4
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. 1.441552 5


Updated August 16, 2019