Instance - rococoC12-010001


	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.

Decomposed These decomposed images were created by GCG.
Name	rococoC11-010100 [MIPLIB]	tbfp-bigm [MIPLIB]	polygonpack4-15 [MIPLIB]	polygonpack4-7 [MIPLIB]	polygonpack4-10 [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 / 0.644	2 / 0.749	3 / 0.761	4 / 0.822	5 / 0.824
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.

Decomposed These decomposed images were created by GCG.
Name	rococoC11-010100 [MIPLIB]	rococoB10-011000 [MIPLIB]	rococoC10-001000 [MIPLIB]	rococoC11-011100 [MIPLIB]	neos-826650 [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 / 0.644	18 / 0.994	56 / 1.119	86 / 1.162	813 / 2.253
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.

Instance Summary

The table below contains summary information for rococoC12-010001, the five most similar instances to rococoC12-010001 according to the MIC, and the five most similar instances to rococoC12-010001 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	rococoC12-010001 [MIPLIB]	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network.	0.000000	-
MIC Top 5	rococoC11-010100 [MIPLIB]	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network.	0.644174	1
	tbfp-bigm [MIPLIB]	Rob Pratt	Two formulations (big-M and network-based) for traveling baseball fan problem. Uses data from 2014 Major League Baseball regular season. Paper uses 2014 data: http://support.sas.com/resources/papers/proceedings14/SAS101-2014.pdf Blog post uses 2015 data: http://blogs.sas.com/content/operations/2015/04/03/the-traveling-baseball-fan-problem/	0.749019	2
	polygonpack4-15 [MIPLIB]	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. Instance 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.	0.760713	3
	polygonpack4-7 [MIPLIB]	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. Instance 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.	0.821885	4
	polygonpack4-10 [MIPLIB]	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. Instance 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.	0.823517	5
MIPLIB Top 5	rococoC11-010100 [MIPLIB]	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network.	0.644174	1
	rococoB10-011000 [MIPLIB]	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network	0.994372	18
	rococoC10-001000 [MIPLIB]	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network	1.119321	56
	rococoC11-011100 [MIPLIB]	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network. Solved by Gurobi 4.5.1 (4 threads) in 66892.47 seconds.	1.161886	86
	neos-826650 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	2.253168	813

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	polygonpack	ustun	rmatr	map	allcolor
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 / 1.324	2 / 1.356	3 / 1.361	4 / 1.362	5 / 1.550

Model Group Summary

The table below contains summary information for the five most similar model groups to rococoC12-010001 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	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.	1.324310	1
	ustun	Berk Ustun	MIP to create optimized data-driven scoring systems. See: https://github.com/ustunb/miplib2017-slim#miplib2017-slim for a description.	1.355728	2
	rmatr	Dmitry Krushinsky	Model coming from a formulation of the p-Median problem using square cost matrices	1.360605	3
	map	Kiyan Ahmadizadeh	Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem	1.361727	4
	allcolor	Domenico Salvagnin	Prepack optimization model.	1.549908	5

Type:	Instance
Submitter:	A. Chabrier, E. Danna, C. Le Pape, L. Perron
Description:	Model for dimensioning the arc capacities in a telecommunication network.
	MIPLIB Entry

Model Group Assignment from MIPLIB:	rococo
Assigned Model Group Rank/ISS in the MIC:	6 / 1.593

rococoC12-010001: Instance-to-Instance Comparison Results

Parent Instance (rococoC12-010001)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

rococoC12-010001: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary