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square
| Type: | Model Group |
| Submitter: | Sascha Kurz |
| Description: | Squaring the square For a given integer n, determine the minimum number of squares in a tiling of an \\(n\\times n\\) square using using only integer sided squares of smaller size. (Although the models get quite large even for moderate n, they can be solved to optimality for all \\(n \\le 61\\), while challenging the MIP solver, especially the presolver.) |
Parent Model Group (square)
All other model groups below were be compared against this "query" model group.  |
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Model Group Composite (MGC) image
Composite of the decomposed CCM images for every instance in the query model group.
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Component Instances (Decomposed)
These are the decomposed CCM images for each instance in the query model group.
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.
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| Name | supportvectormachine | drayage | neos-pseudoapplication-2 | neos-pseudoapplication-89 | neos-pseudoapplication-74 | |
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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.
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1 / 1.397 | 2 / 1.543 | 3 / 1.653 | 4 / 1.660 | 5 / 1.673 |
Model Group Summary
The table below contains summary information for square, and for the five most similar model groups to square according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
|---|---|---|---|---|---|
| Parent Model Group | square | Sascha Kurz | Squaring the square For a given integer n, determine the minimum number of squares in a tiling of an \\(n\\times n\\) square using using only integer sided squares of smaller size. (Although the models get quite large even for moderate n, they can be solved to optimality for all \\(n \\le 61\\), while challenging the MIP solver, especially the presolver.) | 0.000000 | - |
| MIC Top 5 | supportvectormachine | Toni Sorrell | Suport vector machine with ramp loss. GSVM2-RL is the formulation found in Hess E. and Brooks P. (2015) paper, The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization | 1.396914 | 1 |
| drayage | F. Jordan Srour | The .rar file contains three folders: 1) R_mps with all of the models (165, organized into 5 groups R0_, R25_, R50_, R75_, and R100_*), 2) results_and_runtimes with datafiles on the runtime and results, and 3) doc with documentation on the models in the form of a pdf. | 1.542747 | 2 | |
| neos-pseudoapplication-2 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 1.652628 | 3 | |
| neos-pseudoapplication-89 | NEOS Server Submission | Model coming from the NEOS Server with unknown application | 1.660198 | 4 | |
| neos-pseudoapplication-74 | Jeff Linderoth | (None provided) | 1.673080 | 5 |
