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Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach"

Derval Guillaume; Branders Vincent; Dupont Pierre; Schaus Pierre


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{
  "DOI": "10.5281/zenodo.1688740", 
  "abstract": "<p>Synthetic dataset used in &quot;The maximum weighted submatrix coverage problem: A CP approach&quot;.</p>\n\n<p>Includes both the generated datasets as a zip archive and the python script used to generate them.</p>\n\n<p>Each instance is composed of two files in the form</p>\n\n<ul>\n\t<li>XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.</li>\n\t<li>XxY_K_O_0xN_AxB_Ssolution.txt giving the implanted solution. One submatrix per line. Then two JSON arrays follow, separated by a tabulation. The first is the list of rows selected in the submatrix, the second the columns.</li>\n</ul>\n\n<p>With:</p>\n\n<ul>\n\t<li>X and Y the size of the matrix</li>\n\t<li>K the number of submatrices in the implanted solution</li>\n\t<li>O the (minimum) overlap percentage of each submatrix</li>\n\t<li>N the sigma used for the background noise</li>\n\t<li>A and B the size of the implanted submatrices (subject to noise)</li>\n</ul>\n\n<p>&nbsp;</p>", 
  "author": [
    {
      "family": "Derval Guillaume"
    }, 
    {
      "family": "Branders Vincent"
    }, 
    {
      "family": "Dupont Pierre"
    }, 
    {
      "family": "Schaus Pierre"
    }
  ], 
  "id": "1688740", 
  "issued": {
    "date-parts": [
      [
        2018, 
        11, 
        29
      ]
    ]
  }, 
  "publisher": "Zenodo", 
  "title": "Synthetic dataset used in \"The maximum weighted submatrix coverage problem: A CP approach\"", 
  "type": "dataset"
}
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