bnatt400: Instance-to-Instance Comparison Results

Type: Instance
Submitter: Tatsuya Akutsu
Description: Model to identify a singleton attractor in a Boolean network, applications in computational systems biology. Solved by SCIP 3.0 with SoPlex 1.7.0 in half an hour. A Intel Core2 Extreme CPU X9659 @3.00GHz was used.
MIPLIB Entry

Parent Instance (bnatt400)

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

bnatt400 Raw bnatt400 Decomposed bnatt400 Composite of MIC top 5 bnatt400 Composite of MIPLIB top 5 bnatt400 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.
bnatt500 decomposed prod2 decomposed prod1 decomposed dcmulti decomposed neos-5192052-neckar decomposed
Name bnatt500 [MIPLIB] prod2 [MIPLIB] prod1 [MIPLIB] dcmulti [MIPLIB] neos-5192052-neckar [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 / 0.338 2 / 1.036 3 / 1.065 4 / 1.085 5 / 1.091
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
bnatt500 raw prod2 raw prod1 raw dcmulti raw neos-5192052-neckar 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.
bnatt500 decomposed supportcase3 decomposed s1234 decomposed neos-5178119-nalagi decomposed circ10-3 decomposed
Name bnatt500 [MIPLIB] supportcase3 [MIPLIB] s1234 [MIPLIB] neos-5178119-nalagi [MIPLIB] circ10-3 [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 / 0.338 79 / 1.272 499 / 1.607 618 / 1.688 811 / 2.006
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
bnatt500 raw supportcase3 raw s1234 raw neos-5178119-nalagi raw circ10-3 raw

Instance Summary

The table below contains summary information for bnatt400, the five most similar instances to bnatt400 according to the MIC, and the five most similar instances to bnatt400 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance bnatt400 [MIPLIB] Tatsuya Akutsu Model to identify a singleton attractor in a Boolean network, applications in computational systems biology. Solved by SCIP 3.0 with SoPlex 1.7.0 in half an hour. A Intel Core2 Extreme CPU X9659 @3.00GHz was used. 0.000000 -
MIC Top 5 bnatt500 [MIPLIB] Tatsuya Akutsu We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an instance of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009. 0.338358 1
prod2 [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.036391 2
prod1 [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.064936 3
dcmulti [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.084791 4
neos-5192052-neckar [MIPLIB] Jeff Linderoth (None provided) 1.091465 5
MIPLIB Top 5 bnatt500 [MIPLIB] Tatsuya Akutsu We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an instance of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009. 0.338358 1
supportcase3 [MIPLIB] Michael Winkler MIP instances collected from Gurobi forum with unknown application Solved with ParaSCIP in 1551 seconds with 71 solvers(72 cores). 1.272193 79
s1234 [MIPLIB] Siwei Sun These models come from my cryptographic research and are used to search for the best differential characteristics of the round-reduced versions of the block cipher Serpent with the mixed-integer programming technique. For all the models, including S1234.lp, S56701.lp, S456701.lp, I have found a feasible solution in the corresponding mst file. The challenge is that can we find better solutions or can we find the best solutions. 1.606848 499
neos-5178119-nalagi [MIPLIB] Jeff Linderoth (None provided) 1.687981 618
circ10-3 [MIPLIB] M. Winkler Instance from the 2010 SAT conference pseudo-Boolean competition 2.006403 811


bnatt400: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: bnatt
Assigned Model Group Rank/ISS in the MIC: 1 / 0.466

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: bnatt Model group: oocsp-racks Model group: neos-pseudoapplication-74 Model group: scp Model group: mc
Name bnatt oocsp-racks neos-pseudoapplication-74 scp mc
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 / 0.466 2 / 1.450 3 / 1.563 4 / 1.665 5 / 1.705

Model Group Summary

The table below contains summary information for the five most similar model groups to bnatt400 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 bnatt Tatsuya Akutsu We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an model of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009. 0.466149 1
oocsp-racks Gleb Belov These are the models from MiniZinc Challenges 2012-2016 (see www.minizinc.org), compiled for MIP WITH INDICATOR CONSTRAINTS using the develop branch of MiniZinc and CPLEX 12.7.1 on 30 April 2017. Thus, these models can only be handled by solvers accepting indicator constraints. For models compiled with big-M/domain decomposition only, see my previous submission to MIPLIB.To recompile, create a directory MODELS, a list lst12_16.txt of the models with full paths to mzn/dzn files of each model per line, and say$> ~/install/libmzn/tests/benchmarking/mzn-test.py -l ../lst12_16.txt -slvPrf MZN-CPLEX -debug 1 -addOption "-timeout 3 -D fIndConstr=true -D fMIPdomains=false" -useJoinedName "-writeModel MODELS_IND/%s.mps" Alternatively, you can compile individual model as follows: $> mzn-cplex -v -s -G linear -output-time ../challenge_2012_2016/mznc2016_probs/zephyrus/zephyrus.mzn ../challenge_2012_2016/mznc2016_p/zephyrus/14__8__6__3.dzn -a -timeout 3 -D fIndConstr=true -D fMIPdomains=false -writeModel MODELS_IND/challenge_2012_2016mznc2016_probszephyruszephyrusmzn-challenge_2012_2016mznc2016_probszephyrus14__8__6__3dzn.mps 1.450257 2
neos-pseudoapplication-74 Jeff Linderoth (None provided) 1.562942 3
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 1.664672 4
mc F. Ortega, L. Wolsey Fixed cost network flow problems 1.705206 5