lectsched-5-obj: Instance-to-Instance Comparison Results

Type: Instance
Submitter: Harald Schilly
Description: scheduling lectures at university - smaller subset of data with objective to minimize certain overlappings
MIPLIB Entry

Parent Instance (lectsched-5-obj)

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

lectsched-5-obj Raw lectsched-5-obj Decomposed lectsched-5-obj Composite of MIC top 5 lectsched-5-obj Composite of MIPLIB top 5 lectsched-5-obj 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.
lectsched-3 decomposed lectsched-2 decomposed lectsched-1 decomposed probportfolio decomposed tanglegram6 decomposed
Name lectsched-3 [MIPLIB] lectsched-2 [MIPLIB] lectsched-1 [MIPLIB] probportfolio [MIPLIB] tanglegram6 [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 / 0.323 2 / 0.347 3 / 0.364 4 / 0.640 5 / 0.645
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
lectsched-3 raw lectsched-2 raw lectsched-1 raw probportfolio raw tanglegram6 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.
lectsched-3 decomposed lectsched-1 decomposed lectsched-4-obj decomposed l2p12 decomposed neos-4338804-snowy decomposed
Name lectsched-3 [MIPLIB] lectsched-1 [MIPLIB] lectsched-4-obj [MIPLIB] l2p12 [MIPLIB] neos-4338804-snowy [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 / 0.323 3 / 0.364 93 / 1.244 348 / 1.508 645 / 1.810
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
lectsched-3 raw lectsched-1 raw lectsched-4-obj raw l2p12 raw neos-4338804-snowy raw

Instance Summary

The table below contains summary information for lectsched-5-obj, the five most similar instances to lectsched-5-obj according to the MIC, and the five most similar instances to lectsched-5-obj according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance lectsched-5-obj [MIPLIB] Harald Schilly scheduling lectures at university - smaller subset of data with objective to minimize certain overlappings 0.000000 -
MIC Top 5 lectsched-3 [MIPLIB] Harald Schilly University lecture scheduling instance 0.322796 1
lectsched-2 [MIPLIB] Harald Schilly University lecture scheduling instance 0.347133 2
lectsched-1 [MIPLIB] Harald Schilly University lecture scheduling instance 0.364403 3
probportfolio [MIPLIB] Feng Qiu Sample average approximation formulation of a probabilistic portfolio optimization problem. Solved using ug[SCIP/spx], a distributed massively parallel version of SCIP run on 2,000 cores at the HLRN-II super computer facility. 0.639986 4
tanglegram6 [MIPLIB] Falk Hueffner The NP-hard Balanced Subgraph problem (variant of MaxCut) encoded as ILPs. Real-world instances from two applications from bioinformatics, finding monotone subsystems in gene regulatory networks (http://dx.doi.org/10.1007/s10878-009-9212-2) and finding optimal layouts of tanglegrams (http://dx.doi.org/10.1007/978-3-642-11269-0). 0.644834 5
MIPLIB Top 5 lectsched-3 [MIPLIB] Harald Schilly University lecture scheduling instance 0.322796 1
lectsched-1 [MIPLIB] Harald Schilly University lecture scheduling instance 0.364403 3
lectsched-4-obj [MIPLIB] Harald Schilly University lecture scheduling instance 1.244486 93
l2p12 [MIPLIB] Gleb Belov Linearized Constraint Programming models of the MiniZinc Challenges 2012-2016. I should be able to produce versions with indicator constraints supported by Gurobi and CPLEX, however don't know if you can use them and if there is a standard format. These MPS were produced by Gurobi 7.0.2 using the MiniZinc develop branch on eb536656062ca13325a96b5d0881742c7d0e3c38 1.508323 348
neos-4338804-snowy [MIPLIB] Jeff Linderoth (None provided) 1.810415 645


lectsched-5-obj: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: lectsched
Assigned Model Group Rank/ISS in the MIC: 5 / 1.313

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-109 Model group: map Model group: rmatr Model group: polygonpack Model group: lectsched
Name neos-pseudoapplication-109 map rmatr polygonpack lectsched
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 / 1.223 2 / 1.223 3 / 1.254 4 / 1.272 5 / 1.314

Model Group Summary

The table below contains summary information for the five most similar model groups to lectsched-5-obj according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 neos-pseudoapplication-109 Jeff Linderoth (None provided) 1.222708 1
map Kiyan Ahmadizadeh Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem 1.222720 2
rmatr Dmitry Krushinsky Model coming from a formulation of the p-Median problem using square cost matrices 1.253812 3
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. 1.271724 4
lectsched Harald Schilly University lecture scheduling model 1.313986 5


Updated August 16, 2019