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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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    <subfield code="a">&lt;p&gt;Synthetic dataset used in &amp;quot;The maximum weighted submatrix coverage problem: A CP approach&amp;quot;.&lt;/p&gt;

&lt;p&gt;Includes both the generated datasets as a zip archive and the python script used to generate them.&lt;/p&gt;

&lt;p&gt;Each instance is composed of two files in the form&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.&lt;/li&gt;
	&lt;li&gt;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.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;With:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;X and Y the size of the matrix&lt;/li&gt;
	&lt;li&gt;K the number of submatrices in the implanted solution&lt;/li&gt;
	&lt;li&gt;O the (minimum) overlap percentage of each submatrix&lt;/li&gt;
	&lt;li&gt;N the sigma used for the background noise&lt;/li&gt;
	&lt;li&gt;A and B the size of the implanted submatrices (subject to noise)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</subfield>
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