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    <title>PyPI recent updates for varela</title>
    <link>https://pypi.org/project/varela/</link>
    <description>Recent updates to the Python Package Index for varela</description>
    <language>en</language>    <item>
      <title>0.3.2</title>
      <link>https://pypi.org/project/varela/0.3.2/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Thu, 02 Apr 2026 17:17:47 GMT</pubDate>
    </item>    <item>
      <title>0.3.1</title>
      <link>https://pypi.org/project/varela/0.3.1/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Thu, 05 Jun 2025 20:34:25 GMT</pubDate>
    </item>    <item>
      <title>0.3.0</title>
      <link>https://pypi.org/project/varela/0.3.0/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Thu, 05 Jun 2025 15:37:56 GMT</pubDate>
    </item>    <item>
      <title>0.2.9</title>
      <link>https://pypi.org/project/varela/0.2.9/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Sat, 26 Apr 2025 22:29:43 GMT</pubDate>
    </item>    <item>
      <title>0.2.8</title>
      <link>https://pypi.org/project/varela/0.2.8/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 25 Apr 2025 22:08:12 GMT</pubDate>
    </item>    <item>
      <title>0.2.7</title>
      <link>https://pypi.org/project/varela/0.2.7/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 25 Apr 2025 20:10:52 GMT</pubDate>
    </item>    <item>
      <title>0.2.6</title>
      <link>https://pypi.org/project/varela/0.2.6/</link>
      <description>Compute the Approximate Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 25 Apr 2025 19:24:08 GMT</pubDate>
    </item>    <item>
      <title>0.2.5</title>
      <link>https://pypi.org/project/varela/0.2.5/</link>
      <description>Compute the Exact Minimum Vertex Cover for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Wed, 19 Mar 2025 09:56:05 GMT</pubDate>
    </item>    <item>
      <title>0.2.4</title>
      <link>https://pypi.org/project/varela/0.2.4/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of at most 1.75 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 28 Feb 2025 18:43:13 GMT</pubDate>
    </item>    <item>
      <title>0.2.3</title>
      <link>https://pypi.org/project/varela/0.2.3/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of at most 1.75 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Thu, 27 Feb 2025 21:35:53 GMT</pubDate>
    </item>    <item>
      <title>0.2.2</title>
      <link>https://pypi.org/project/varela/0.2.2/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of less than 2 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Wed, 26 Feb 2025 07:11:37 GMT</pubDate>
    </item>    <item>
      <title>0.2.1</title>
      <link>https://pypi.org/project/varela/0.2.1/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of at most 1.9 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Tue, 25 Feb 2025 20:03:45 GMT</pubDate>
    </item>    <item>
      <title>0.2.0</title>
      <link>https://pypi.org/project/varela/0.2.0/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of at most 1.9 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Tue, 25 Feb 2025 17:02:58 GMT</pubDate>
    </item>    <item>
      <title>0.1.9</title>
      <link>https://pypi.org/project/varela/0.1.9/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor less than 2 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Tue, 25 Feb 2025 07:00:34 GMT</pubDate>
    </item>    <item>
      <title>0.1.8</title>
      <link>https://pypi.org/project/varela/0.1.8/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor less than 2 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Mon, 24 Feb 2025 12:59:54 GMT</pubDate>
    </item>    <item>
      <title>0.1.7</title>
      <link>https://pypi.org/project/varela/0.1.7/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor less than 2 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Mon, 24 Feb 2025 11:13:55 GMT</pubDate>
    </item>    <item>
      <title>0.1.6</title>
      <link>https://pypi.org/project/varela/0.1.6/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor less than 2 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Tue, 18 Feb 2025 13:45:30 GMT</pubDate>
    </item>    <item>
      <title>0.1.5</title>
      <link>https://pypi.org/project/varela/0.1.5/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor at most 3/2 for undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Sun, 16 Feb 2025 04:12:20 GMT</pubDate>
    </item>    <item>
      <title>0.1.4</title>
      <link>https://pypi.org/project/varela/0.1.4/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of &lt; √2 for large enough graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Sat, 15 Feb 2025 15:49:08 GMT</pubDate>
    </item>    <item>
      <title>0.1.3</title>
      <link>https://pypi.org/project/varela/0.1.3/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of &lt; 2 for an undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Sat, 15 Feb 2025 00:07:54 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/varela/0.1.2/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of &lt; 2 for an undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 14 Feb 2025 21:48:07 GMT</pubDate>
    </item>    <item>
      <title>0.1.1</title>
      <link>https://pypi.org/project/varela/0.1.1/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of &lt; 2 for an undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 14 Feb 2025 18:25:13 GMT</pubDate>
    </item>    <item>
      <title>0.1.0</title>
      <link>https://pypi.org/project/varela/0.1.0/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 14 Feb 2025 07:44:03 GMT</pubDate>
    </item>    <item>
      <title>0.0.9</title>
      <link>https://pypi.org/project/varela/0.0.9/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 14 Feb 2025 07:29:26 GMT</pubDate>
    </item>    <item>
      <title>0.0.8</title>
      <link>https://pypi.org/project/varela/0.0.8/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded in DIMACS format.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 14 Feb 2025 07:12:49 GMT</pubDate>
    </item>    <item>
      <title>0.0.7</title>
      <link>https://pypi.org/project/varela/0.0.7/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded in DIMACS format and stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 14 Feb 2025 01:54:31 GMT</pubDate>
    </item>    <item>
      <title>0.0.6</title>
      <link>https://pypi.org/project/varela/0.0.6/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded as a Boolean adjacency matrix stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 07 Feb 2025 19:29:58 GMT</pubDate>
    </item>    <item>
      <title>0.0.5</title>
      <link>https://pypi.org/project/varela/0.0.5/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded as a Boolean adjacency matrix stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 07 Feb 2025 16:01:00 GMT</pubDate>
    </item>    <item>
      <title>0.0.4</title>
      <link>https://pypi.org/project/varela/0.0.4/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 3/2 for an undirected graph encoded as a Boolean adjacency matrix stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Fri, 07 Feb 2025 15:08:07 GMT</pubDate>
    </item>    <item>
      <title>0.0.3</title>
      <link>https://pypi.org/project/varela/0.0.3/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of ≤ 7/5 for large enough undirected graphs encoded as a Boolean adjacency matrix stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Wed, 05 Feb 2025 21:35:31 GMT</pubDate>
    </item>    <item>
      <title>0.0.2</title>
      <link>https://pypi.org/project/varela/0.0.2/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of 7/5 for large enough undirected graphs encoded as a Boolean adjacency matrix stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Wed, 05 Feb 2025 20:12:58 GMT</pubDate>
    </item>    <item>
      <title>0.0.1</title>
      <link>https://pypi.org/project/varela/0.0.1/</link>
      <description>Estimating the Minimum Vertex Cover with an approximation factor of 7/5 for large enough undirected graphs encoded as a Boolean adjacency matrix stored in a file.</description>
<author>vega.frank@gmail.com</author>      <pubDate>Wed, 05 Feb 2025 12:11:38 GMT</pubDate>
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