milo-v12-6-r1-75-1: Instance-to-Instance Comparison Results

Type: Instance
Submitter: Tamas Terlaky
Description: The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time.
MIPLIB Entry

Parent Instance (milo-v12-6-r1-75-1)

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

milo-v12-6-r1-75-1 Raw milo-v12-6-r1-75-1 Decomposed milo-v12-6-r1-75-1 Composite of MIC top 5 milo-v12-6-r1-75-1 Composite of MIPLIB top 5 milo-v12-6-r1-75-1 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.
milo-v12-6-r1-58-1 decomposed milo-v12-6-r2-40-1 decomposed snp-04-052-052 decomposed snp-02-004-104 decomposed ab51-40-100 decomposed
Name milo-v12-6-r1-58-1 [MIPLIB] milo-v12-6-r2-40-1 [MIPLIB] snp-04-052-052 [MIPLIB] snp-02-004-104 [MIPLIB] ab51-40-100 [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 / 0.206 2 / 0.552 3 / 0.616 4 / 0.653 5 / 0.665
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
milo-v12-6-r1-58-1 raw milo-v12-6-r2-40-1 raw snp-04-052-052 raw snp-02-004-104 raw ab51-40-100 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.
milo-v12-6-r1-58-1 decomposed milo-v12-6-r2-40-1 decomposed milo-v13-4-3d-4-0 decomposed milo-v13-4-3d-3-0 decomposed neos-4292145-piako decomposed
Name milo-v12-6-r1-58-1 [MIPLIB] milo-v12-6-r2-40-1 [MIPLIB] milo-v13-4-3d-4-0 [MIPLIB] milo-v13-4-3d-3-0 [MIPLIB] neos-4292145-piako [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 / 0.206 2 / 0.552 68 / 0.815 125 / 0.901 680 / 1.690
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
milo-v12-6-r1-58-1 raw milo-v12-6-r2-40-1 raw milo-v13-4-3d-4-0 raw milo-v13-4-3d-3-0 raw neos-4292145-piako raw

Instance Summary

The table below contains summary information for milo-v12-6-r1-75-1, the five most similar instances to milo-v12-6-r1-75-1 according to the MIC, and the five most similar instances to milo-v12-6-r1-75-1 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance milo-v12-6-r1-75-1 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.000000 -
MIC Top 5 milo-v12-6-r1-58-1 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.206392 1
milo-v12-6-r2-40-1 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.551708 2
snp-04-052-052 [MIPLIB] Gerald Gamrath Supply network planning problems. 0.615830 3
snp-02-004-104 [MIPLIB] Gerald Gamrath Supply network planning problems. 0.652996 4
ab51-40-100 [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 0.664887 5
MIPLIB Top 5 milo-v12-6-r1-58-1 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.206392 1
milo-v12-6-r2-40-1 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.551708 2
milo-v13-4-3d-4-0 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.814598 68
milo-v13-4-3d-3-0 [MIPLIB] Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 instances, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 0.900720 125
neos-4292145-piako [MIPLIB] Jeff Linderoth (None provided) 1.689768 680


milo-v12-6-r1-75-1: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: milo
Assigned Model Group Rank/ISS in the MIC: 30 / 1.583

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-2 Model group: sp_product Model group: ab Model group: seqsolve Model group: noip
Name neos-pseudoapplication-2 sp_product ab seqsolve noip
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 / 1.012 2 / 1.045 3 / 1.070 4 / 1.131 5 / 1.133

Model Group Summary

The table below contains summary information for the five most similar model groups to milo-v12-6-r1-75-1 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 neos-pseudoapplication-2 NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.011610 1
sp_product MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.044788 2
ab MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.069894 3
seqsolve Irv Lustig The 3 problems in this group (seqsolve1-seqsolve3) represent a hierarchical optimization process, which is derived from a customer problem for assigning people to sites into blocks of time on days of the week. The specialty of this submission is that the best known solution for seqsolveX can be used as a MIP start for seqsolveX+1. For a description of the connections between the problems, please refer to the README.txt contained in the model data for this submission, which also includes MIP start files and a Gurobi log file. 1.130704 4
noip Christopher Hojny integer programming formulation that verifies that no integer programming formulation of a given 0/1-point set exists 1.132518 5


Updated August 16, 2019