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

<p>Synthetic dataset used in &quot;The maximum weighted submatrix coverage problem: A CP approach&quot;.</p> <p>Includes both the generated datasets as a zip archive and the python script used to generate them.</p> <p>Each instance is composed of two files in the form</p> <ul> <li>XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.</li> <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> </ul> <p>With:</p> <ul> <li>X and Y the size of the matrix</li> <li>K the number of submatrices in the implanted solution</li> <li>O the (minimum) overlap percentage of each submatrix</li> <li>N the sigma used for the background noise</li> <li>A and B the size of the implanted submatrices (subject to noise)</li> </ul> <p>&nbsp;</p>