lectsched

Type: Model Group
Submitter: Harald Schilly
Description: University lecture scheduling model

Parent Model Group (lectsched)

All other model groups below were be compared against this "query" model group.

Model group: lectsched
Model Group Composite (MGC) image Composite of the decomposed CCM images for every instance in the query model group.

Component Instances (Decomposed)

These are the decomposed CCM images for each instance in the query model group.

These are component instance images.
Component instance: lectsched-5-obj Component instance: lectsched-1 Component instance: lectsched-3 Component instance: lectsched-2 Component instance: lectsched-4-obj
Name lectsched-5-obj lectsched-1 lectsched-3 lectsched-2 lectsched-4-obj

MIC Top 5 Model Groups

These are the 5 MGC images that are most similar to the MGC image for the query model group, according to the ISS metric.

FIXME - These are model group composite images.
Model group: huefner Model group: allcolor Model group: neos-pseudoapplication-109 Model group: cryptanalysis Model group: polygonpack
Name huefner allcolor neos-pseudoapplication-109 cryptanalysis polygonpack
Rank / ISS The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query model group and all other model groups. A smaller ISS value indicates greater similarity.
1 / 1.538 2 / 1.662 3 / 1.680 4 / 1.681 5 / 1.702

Model Group Summary

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

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
Parent Model Group lectsched Harald Schilly University lecture scheduling model 0.000000 -
MIC Top 5 huefner Falk Hueffner The NP-hard Balanced Subgraph problem (variant of MaxCut) encoded as ILPs. Real-world models 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). 1.537793 1
allcolor Domenico Salvagnin Prepack optimization model. 1.662482 2
neos-pseudoapplication-109 Jeff Linderoth (None provided) 1.679574 3
cryptanalysis 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 1.680924 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. 1.701609 5