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<!DOCTYPE html><html lang="en">
<head>
<meta charset="utf-8"/>
<title>List of median selection networks</title>
<meta name = "keywords" content = "Median selection networks, list, Median circuits" />
<meta name = "description" content = "List of median selection networks for a given number of inputs" />
<meta name = "author" content = "Bert Dobbelaere" />
<link rel="icon" href="sorticon32.png" sizes="32x32">
<style>
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background-color: linen;
}
table {
border-collapse: collapse;
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padding: 10px 20px 10px 20px;
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td {
padding: 20px 20px 20px 20px;
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td.idx {
padding: 2px 20px 2px 20px;
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font-family: "Lucida Console", monospace;
font-size: 80%;
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</style>
</head><body>
<h2>Median selection networks for a given number of inputs</h2>
<p>The networks on this page can be used to find the <b>median value</b> of the inputs and are not full sorting networks.</p>
<p>The rows that yield the output(s) are marked in blue. For an even number of inputs, the average of the two outputs still needs to be computed by the application. Note that in this case, the two outputs are usually not ordered.<br>A median selection network with an even number of inputs can be converted into a "perfect halver" network by adding a single CE element connecting the two central outputs, however the networks on this page are not optimized for this.</p>
<p>Just like for the <a href="sorting_networks.html">sorting networks list</a>, the median selection networks have been defined in terms of two metrics:
<ul><li>Number of compare/exchange elements (CEs) - metric below called "<b>size</b>"</li>
<li>Number of parallel operation steps (layers), determining processing latency - metric below called "<b>depth</b>"</li></ul></p>
<p>Provided is a minimum set of networks that each can be the best choice depending on how both criteria are weighted.</p>
<p>The additional "<b>cost</b>" metric is derived from the idea that each CE can be considered as a combination of a minimizer and a maximizer.<br>If both results are used, its cost is 2. If one of the outputs is discarded, one of the subcomponents can be removed and the cost is 1. The "cost" metric corresponds to the number of subcomponents as described in [<a href="#Sekan04">Sekan04</a>].</p>
<p>If no source is mentioned, the network was generated by the <a href="https://github.com/bertdobbelaere/SorterHunter">SorterHunter</a> program, by applying postfix networks to the evolving network and removing them from the resulting sorting network.
</p>
<p>In many cases, networks could also be derived from other median selection networks with a slightly different number of inputs:
<ul><li>Removing two inputs from a network for N+2 inputs.</li>
<li>For odd N, adding a central pair connecting the outputs of a network for N+1 inputs and then removing one input from the resulting network.</li>
<li>For odd N, starting from a network for N-1 inputs, add a 3-sorter to its two outputs and one new input.</li></ul></p>
<p>For questions, remarks, or to contribute improved results please contact bert.o.dobbelaere[at]telenet[dot]be.</p><h3>Summary table</h3>
<table><tr><th>Number of inputs</th><th>(Size,Depth) combinations</th><th>Comments</th></tr>
<tr><td class="idx">3</td><td class="idx"><a href="#N3L3D3">(3, 3)</a> </td><td class="idx">For 3 inputs, the optimal median selection network is just the optimal sorting network.</td></tr>
<tr><td class="idx">4</td><td class="idx"><a href="#N4L4D2">(4, 2)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">5</td><td class="idx"><a href="#N5L7D5">(7, 5)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">6</td><td class="idx"><a href="#N6L10D4">(10, 4)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">7</td><td class="idx"><a href="#N7L13D6">(13, 6)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">8</td><td class="idx"><a href="#N8L16D5">(16, 5)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">9</td><td class="idx"><a href="#N9L19D7">(19, 7)</a> </td><td class="idx">Optimality from [<a href="#Smith96">Smith96</a>]</td></tr>
<tr><td class="idx">10</td><td class="idx"><a href="#N10L22D8">(22, 8)</a> <a href="#N10L23D7">(23, 7)</a> <a href="#N10L24D6">(24, 6)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">11</td><td class="idx"><a href="#N11L25D11">(25, 11)</a> <a href="#N11L26D8">(26, 8)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">12</td><td class="idx"><a href="#N12L29D7">(29, 7)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">13</td><td class="idx"><a href="#N13L32D10">(32, 10)</a> <a href="#N13L33D9">(33, 9)</a> </td><td class="idx">Best cost is mentioned in [<a href="#Sekan04">Sekan04</a>] as >52, while <a href="https://github.com/bertdobbelaere/SorterHunter">SorterHunter</a> reached equality (=52).</td></tr>
<tr><td class="idx">14</td><td class="idx"><a href="#N14L38D10">(38, 10)</a> <a href="#N14L39D9">(39, 9)</a> <a href="#N14L40D8">(40, 8)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">15</td><td class="idx"><a href="#N15L41D12">(41, 12)</a> <a href="#N15L42D11">(42, 11)</a> <a href="#N15L43D10">(43, 10)</a> <a href="#N15L46D9">(46, 9)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">16</td><td class="idx"><a href="#N16L46D10">(46, 10)</a> <a href="#N16L47D9">(47, 9)</a> <a href="#N16L49D8">(49, 8)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">17</td><td class="idx"><a href="#N17L49D12">(49, 12)</a> <a href="#N17L51D11">(51, 11)</a> <a href="#N17L56D10">(56, 10)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">18</td><td class="idx"><a href="#N18L55D12">(55, 12)</a> <a href="#N18L56D10">(56, 10)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">19</td><td class="idx"><a href="#N19L58D15">(58, 15)</a> <a href="#N19L59D12">(59, 12)</a> <a href="#N19L60D11">(60, 11)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">20</td><td class="idx"><a href="#N20L63D12">(63, 12)</a> <a href="#N20L64D10">(64, 10)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">21</td><td class="idx"><a href="#N21L66D15">(66, 15)</a> <a href="#N21L67D12">(67, 12)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">22</td><td class="idx"><a href="#N22L74D14">(74, 14)</a> <a href="#N22L75D12">(75, 12)</a> <a href="#N22L76D11">(76, 11)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">23</td><td class="idx"><a href="#N23L77D16">(77, 16)</a> <a href="#N23L78D14">(78, 14)</a> <a href="#N23L80D13">(80, 13)</a> <a href="#N23L83D12">(83, 12)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">24</td><td class="idx"><a href="#N24L82D14">(82, 14)</a> <a href="#N24L83D12">(83, 12)</a> <a href="#N24L86D11">(86, 11)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">25</td><td class="idx"><a href="#N25L85D16">(85, 16)</a> <a href="#N25L86D15">(86, 15)</a> <a href="#N25L87D14">(87, 14)</a> <a href="#N25L97D13">(97, 13)</a> </td><td class="idx">In [<a href="#VS08">VS08</a>] the optimal size was listed as 99. It is assumed that this was just the best of generic construction methods. The method explained in [<a href="#Adams21">Adams21</a>] also results in 99 swaps.</td></tr>
<tr><td class="idx">26</td><td class="idx"><a href="#N26L94D16">(94, 16)</a> <a href="#N26L95D14">(95, 14)</a> <a href="#N26L96D13">(96, 13)</a> <a href="#N26L104D12">(104, 12)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">27</td><td class="idx"><a href="#N27L97D18">(97, 18)</a> <a href="#N27L98D17">(98, 17)</a> <a href="#N27L99D15">(99, 15)</a> <a href="#N27L102D14">(102, 14)</a> </td><td class="idx">Trimmed result starting from pairwise sorting network in [<a href="#Adams21">Adams21</a>] needs 111 CEs.</td></tr>
<tr><td class="idx">28</td><td class="idx"><a href="#N28L104D14">(104, 14)</a> <a href="#N28L105D13">(105, 13)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">29</td><td class="idx"><a href="#N29L107D17">(107, 17)</a> <a href="#N29L108D16">(108, 16)</a> <a href="#N29L115D15">(115, 15)</a> <a href="#N29L118D14">(118, 14)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">30</td><td class="idx"><a href="#N30L118D16">(118, 16)</a> <a href="#N30L119D14">(119, 14)</a> <a href="#N30L122D13">(122, 13)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">31</td><td class="idx"><a href="#N31L121D19">(121, 19)</a> <a href="#N31L122D17">(122, 17)</a> <a href="#N31L125D15">(125, 15)</a> <a href="#N31L128D14">(128, 14)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">32</td><td class="idx"><a href="#N32L128D14">(128, 14)</a> <a href="#N32L132D13">(132, 13)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">33</td><td class="idx"><a href="#N33L131D17">(131, 17)</a> <a href="#N33L135D16">(135, 16)</a> <a href="#N33L139D15">(139, 15)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">34</td><td class="idx"><a href="#N34L140D18">(140, 18)</a> <a href="#N34L141D16">(141, 16)</a> <a href="#N34L142D15">(142, 15)</a> <a href="#N34L143D14">(143, 14)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">35</td><td class="idx"><a href="#N35L143D21">(143, 21)</a> <a href="#N35L144D19">(144, 19)</a> <a href="#N35L145D18">(145, 18)</a> <a href="#N35L146D17">(146, 17)</a> <a href="#N35L148D16">(148, 16)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">36</td><td class="idx"><a href="#N36L150D18">(150, 18)</a> <a href="#N36L152D15">(152, 15)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">37</td><td class="idx"><a href="#N37L153D21">(153, 21)</a> <a href="#N37L155D18">(155, 18)</a> <a href="#N37L162D17">(162, 17)</a> <a href="#N37L168D16">(168, 16)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">38</td><td class="idx"><a href="#N38L164D17">(164, 17)</a> <a href="#N38L166D16">(166, 16)</a> <a href="#N38L172D15">(172, 15)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">39</td><td class="idx"><a href="#N39L167D20">(167, 20)</a> <a href="#N39L169D19">(169, 19)</a> <a href="#N39L170D18">(170, 18)</a> <a href="#N39L172D17">(172, 17)</a> <a href="#N39L178D16">(178, 16)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">40</td><td class="idx"><a href="#N40L174D17">(174, 17)</a> <a href="#N40L176D16">(176, 16)</a> <a href="#N40L182D15">(182, 15)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">41</td><td class="idx"><a href="#N41L177D20">(177, 20)</a> <a href="#N41L179D19">(179, 19)</a> <a href="#N41L185D18">(185, 18)</a> <a href="#N41L194D17">(194, 17)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">42</td><td class="idx"><a href="#N42L191D21">(191, 21)</a> <a href="#N42L192D17">(192, 17)</a> <a href="#N42L198D16">(198, 16)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">43</td><td class="idx"><a href="#N43L194D24">(194, 24)</a> <a href="#N43L195D20">(195, 20)</a> <a href="#N43L198D18">(198, 18)</a> <a href="#N43L204D17">(204, 17)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">44</td><td class="idx"><a href="#N44L202D17">(202, 17)</a> <a href="#N44L208D16">(208, 16)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">45</td><td class="idx"><a href="#N45L205D20">(205, 20)</a> <a href="#N45L211D19">(211, 19)</a> <a href="#N45L220D18">(220, 18)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">46</td><td class="idx"><a href="#N46L218D22">(218, 22)</a> <a href="#N46L220D20">(220, 20)</a> <a href="#N46L222D18">(222, 18)</a> <a href="#N46L225D17">(225, 17)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">47</td><td class="idx"><a href="#N47L221D25">(221, 25)</a> <a href="#N47L223D23">(223, 23)</a> <a href="#N47L225D21">(225, 21)</a> <a href="#N47L228D19">(228, 19)</a> <a href="#N47L232D18">(232, 18)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">48</td><td class="idx"><a href="#N48L228D22">(228, 22)</a> <a href="#N48L230D20">(230, 20)</a> <a href="#N48L232D18">(232, 18)</a> <a href="#N48L237D17">(237, 17)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">49</td><td class="idx"><a href="#N49L231D25">(231, 25)</a> <a href="#N49L233D23">(233, 23)</a> <a href="#N49L235D21">(235, 21)</a> <a href="#N49L240D20">(240, 20)</a> <a href="#N49L274D19">(274, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">50</td><td class="idx"><a href="#N50L245D27">(245, 27)</a> <a href="#N50L246D22">(246, 22)</a> <a href="#N50L247D21">(247, 21)</a> <a href="#N50L249D20">(249, 20)</a> <a href="#N50L272D19">(272, 19)</a> <a href="#N50L280D18">(280, 18)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">51</td><td class="idx"><a href="#N51L248D30">(248, 30)</a> <a href="#N51L249D25">(249, 25)</a> <a href="#N51L250D24">(250, 24)</a> <a href="#N51L252D23">(252, 23)</a> <a href="#N51L253D22">(253, 22)</a> <a href="#N51L255D21">(255, 21)</a> <a href="#N51L280D20">(280, 20)</a> <a href="#N51L288D19">(288, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">52</td><td class="idx"><a href="#N52L255D27">(255, 27)</a> <a href="#N52L256D22">(256, 22)</a> <a href="#N52L257D21">(257, 21)</a> <a href="#N52L259D20">(259, 20)</a> <a href="#N52L286D19">(286, 19)</a> <a href="#N52L294D18">(294, 18)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">53</td><td class="idx"><a href="#N53L258D30">(258, 30)</a> <a href="#N53L259D25">(259, 25)</a> <a href="#N53L260D24">(260, 24)</a> <a href="#N53L262D23">(262, 23)</a> <a href="#N53L273D21">(273, 21)</a> <a href="#N53L297D20">(297, 20)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">54</td><td class="idx"><a href="#N54L276D23">(276, 23)</a> <a href="#N54L278D22">(278, 22)</a> <a href="#N54L279D21">(279, 21)</a> <a href="#N54L280D20">(280, 20)</a> <a href="#N54L302D19">(302, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">55</td><td class="idx"><a href="#N55L279D26">(279, 26)</a> <a href="#N55L281D23">(281, 23)</a> <a href="#N55L285D21">(285, 21)</a> <a href="#N55L309D20">(309, 20)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">56</td><td class="idx"><a href="#N56L286D23">(286, 23)</a> <a href="#N56L288D22">(288, 22)</a> <a href="#N56L290D21">(290, 21)</a> <a href="#N56L292D20">(292, 20)</a> <a href="#N56L314D19">(314, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">57</td><td class="idx"><a href="#N57L289D26">(289, 26)</a> <a href="#N57L291D25">(291, 25)</a> <a href="#N57L293D23">(293, 23)</a> <a href="#N57L297D21">(297, 21)</a> <a href="#N57L321D20">(321, 20)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">58</td><td class="idx"><a href="#N58L301D23">(301, 23)</a> <a href="#N58L302D20">(302, 20)</a> <a href="#N58L326D19">(326, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">59</td><td class="idx"><a href="#N59L304D26">(304, 26)</a> <a href="#N59L305D23">(305, 23)</a> <a href="#N59L309D21">(309, 21)</a> <a href="#N59L333D20">(333, 20)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">60</td><td class="idx"><a href="#N60L313D23">(313, 23)</a> <a href="#N60L314D20">(314, 20)</a> <a href="#N60L338D19">(338, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">61</td><td class="idx"><a href="#N61L316D26">(316, 26)</a> <a href="#N61L317D23">(317, 23)</a> <a href="#N61L321D21">(321, 21)</a> <a href="#N61L345D20">(345, 20)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">62</td><td class="idx"><a href="#N62L325D23">(325, 23)</a> <a href="#N62L326D20">(326, 20)</a> <a href="#N62L350D19">(350, 19)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">63</td><td class="idx"><a href="#N63L328D26">(328, 26)</a> <a href="#N63L329D23">(329, 23)</a> <a href="#N63L333D21">(333, 21)</a> <a href="#N63L357D20">(357, 20)</a> </td><td class="idx"></td></tr>
<tr><td class="idx">64</td><td class="idx"><a href="#N64L337D23">(337, 23)</a> <a href="#N64L338D20">(338, 20)</a> <a href="#N64L362D19">(362, 19)</a> </td><td class="idx"></td></tr>
</table>
<h3>Individual networks:</h3>
<table id=Networks>
<tr id="N3L3D3"><td>Median selection network for 3 inputs, 3 CEs, 3 layers (cost=4):<br>
<p class="mono">[(0,1)]<br>
[(1,2)]<br>
[(0,1)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N4L4D2"><td>Median selection network for 4 inputs, 4 CEs, 2 layers (cost=6):<br>
<p class="mono">[(0,1),(2,3)]<br>
[(0,2),(1,3)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N5L7D5"><td>Median selection network for 5 inputs, 7 CEs, 5 layers (cost=10):<br>
<p class="mono">[(0,1),(2,3)]<br>
[(0,2),(1,3)]<br>
[(2,4)]<br>
[(1,2)]<br>
[(2,4)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N6L10D4"><td>Median selection network for 6 inputs, 10 CEs, 4 layers (cost=16):<br>
<p class="mono">[(0,1),(4,5)]<br>
[(0,5),(1,3),(2,4)]<br>
[(0,2),(1,4),(3,5)]<br>
[(1,2),(3,4)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N7L13D6"><td>Median selection network for 7 inputs, 13 CEs, 6 layers (cost=20):<br>
<p class="mono">[(0,6),(1,2),(3,4)]<br>
[(0,2),(1,4),(3,5)]<br>
[(0,1),(2,5),(4,6)]<br>
[(1,3),(2,4)]<br>
[(3,4)]<br>
[(2,3)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N8L16D5"><td>Median selection network for 8 inputs, 16 CEs, 5 layers (cost=26):<br>
<p class="mono">[(0,2),(1,3),(4,6),(5,7)]<br>
[(0,4),(1,5),(2,6),(3,7)]<br>
[(0,1),(2,4),(3,5),(6,7)]<br>
[(2,3),(4,5)]<br>
[(1,4),(3,6)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N9L19D7"><td>Median selection network for 9 inputs, 19 CEs, 7 layers (cost=30):<br>
<p class="mono">[(0,7),(1,2),(3,5),(4,8)]<br>
[(0,2),(1,5),(3,8),(4,7)]<br>
[(0,3),(1,4),(2,8),(5,7)]<br>
[(3,4),(5,6)]<br>
[(2,5),(4,6)]<br>
[(2,3),(4,5)]<br>
[(3,4)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N10L22D8"><td>Median selection network for 10 inputs, 22 CEs, 8 layers (cost=36):<br>
<p class="mono">[(0,1),(3,5),(4,6),(8,9)]<br>
[(0,3),(1,5),(4,8),(6,9)]<br>
[(1,3),(6,8)]<br>
[(0,6),(1,4),(3,9),(5,8)]<br>
[(2,6),(3,7)]<br>
[(2,3),(6,7)]<br>
[(3,4),(5,6)]<br>
[(3,5),(4,6)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N10L23D7"><td>Median selection network for 10 inputs, 23 CEs, 7 layers (cost=38):<br>
<p class="mono">[(0,9),(2,5),(4,7)]<br>
[(0,3),(2,4),(5,7),(6,9)]<br>
[(0,4),(1,3),(5,9),(6,8)]<br>
[(0,2),(1,5),(4,8),(7,9)]<br>
[(1,4),(2,6),(3,7),(5,8)]<br>
[(3,5),(4,6)]<br>
[(3,4),(5,6)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N10L24D6"><td>Median selection network for 10 inputs, 24 CEs, 6 layers (cost=40):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9)]<br>
[(0,5),(1,8),(2,6),(3,7),(4,9)]<br>
[(0,2),(1,4),(3,6),(5,8),(7,9)]<br>
[(2,7),(3,5),(4,6)]<br>
[(1,2),(3,4),(5,6),(7,8)]<br>
[(2,4),(5,7)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N11L25D11"><td>Median selection network for 11 inputs, 25 CEs, 11 layers (cost=40):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9)]<br>
[(0,2),(1,3),(4,6),(5,7)]<br>
[(1,2),(5,6)]<br>
[(0,5),(1,4),(2,7),(3,6)]<br>
[(2,5),(3,4)]<br>
[(2,8),(4,9)]<br>
[(8,10)]<br>
[(3,8),(5,10)]<br>
[(4,5)]<br>
[(4,8)]<br>
[(5,8)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N11L26D8"><td>Median selection network for 11 inputs, 26 CEs, 8 layers (cost=42):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9)]<br>
[(0,6),(1,5),(2,4),(3,7),(8,10)]<br>
[(0,8),(3,10),(4,6),(5,7)]<br>
[(1,3),(4,9),(6,10)]<br>
[(1,8),(2,4),(3,6),(5,9)]<br>
[(3,5),(4,8)]<br>
[(3,8),(5,6)]<br>
[(5,8)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N12L29D7"><td>Median selection network for 12 inputs, 29 CEs, 7 layers (cost=48):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11)]<br>
[(0,4),(1,10),(2,9),(5,6),(7,11)]<br>
[(2,6),(3,7),(4,8),(5,9)]<br>
[(1,5),(2,8),(3,9),(6,10)]<br>
[(3,5),(6,8)]<br>
[(3,6),(5,8)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N13L32D10"><td>Median selection network for 13 inputs, 32 CEs, 10 layers (cost=52):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11)]<br>
[(0,8),(1,9),(2,4),(3,5),(6,10),(7,11)]<br>
[(0,6),(1,7),(3,4),(8,10),(9,11)]<br>
[(2,6),(3,10),(4,8),(5,9)]<br>
[(1,3),(4,6),(7,8)]<br>
[(3,12),(5,6)]<br>
[(3,5),(6,12)]<br>
[(5,7),(6,10)]<br>
[(5,6)]<br>
[(6,7)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N13L33D9"><td>Median selection network for 13 inputs, 33 CEs, 9 layers (cost=54):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11)]<br>
[(0,8),(1,10),(2,9),(3,11),(5,12)]<br>
[(1,4),(2,12),(3,6),(5,10),(7,9)]<br>
[(2,5),(3,12),(4,7)]<br>
[(3,8),(4,5),(6,12),(7,10)]<br>
[(5,8),(6,7)]<br>
[(6,8)]<br>
[(5,6)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N14L38D10"><td>Median selection network for 14 inputs, 38 CEs, 10 layers (cost=64):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(0,9),(2,10),(3,11),(4,13)]<br>
[(0,12),(1,13),(4,6),(7,9)]<br>
[(0,4),(1,10),(3,12),(5,7),(6,8),(9,13)]<br>
[(1,8),(2,4),(5,12),(9,11)]<br>
[(1,5),(3,4),(8,12),(9,10)]<br>
[(3,5),(4,9),(8,10)]<br>
[(4,6),(7,9)]<br>
[(5,7),(6,8)]<br>
[(5,6),(7,8)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N14L39D9"><td>Median selection network for 14 inputs, 39 CEs, 9 layers (cost=66):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(1,3),(2,7),(4,8),(5,9),(6,11),(10,12)]<br>
[(1,6),(3,8),(5,10),(7,12)]<br>
[(1,4),(3,7),(6,10),(9,12)]<br>
[(0,4),(2,6),(7,11),(9,13)]<br>
[(0,3),(2,5),(4,7),(6,9),(8,11),(10,13)]<br>
[(3,5),(4,6),(7,9),(8,10)]<br>
[(5,7),(6,8)]<br>
[(5,6),(7,8)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N14L40D8"><td>Median selection network for 14 inputs, 40 CEs, 8 layers (cost=68):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(0,7),(1,12),(2,8),(3,9),(4,10),(5,11),(6,13)]<br>
[(0,1),(2,4),(3,8),(5,10),(9,11),(12,13)]<br>
[(0,11),(1,9),(2,13),(4,12),(5,6),(7,8)]<br>
[(0,4),(1,5),(3,6),(7,10),(8,12),(9,13)]<br>
[(3,5),(4,7),(6,9),(8,10)]<br>
[(5,6),(7,8)]<br>
[(5,7),(6,8)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N15L41D12"><td>Median selection network for 15 inputs, 41 CEs, 12 layers (cost=68):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(0,12),(1,13),(2,8),(3,9),(10,14)]<br>
[(3,11),(10,12)]<br>
[(2,10),(3,6),(5,11),(7,12)]<br>
[(0,3),(4,10),(5,6),(7,8),(9,12)]<br>
[(1,5),(3,10),(6,13),(8,11)]<br>
[(1,3),(5,8),(6,10)]<br>
[(3,7),(5,14),(6,9)]<br>
[(4,7),(5,6),(8,14),(9,10)]<br>
[(6,7),(8,9)]<br>
[(6,8)]<br>
[(7,8)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N15L42D11"><td>Median selection network for 15 inputs, 42 CEs, 11 layers (cost=70):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11)]<br>
[(0,11),(1,14),(2,5),(6,9)]<br>
[(0,4),(1,12),(5,10),(7,11),(13,14)]<br>
[(1,8),(6,12),(7,14),(9,13)]<br>
[(2,6),(3,13),(4,8),(5,9),(10,12)]<br>
[(3,7),(6,9),(8,10)]<br>
[(3,4),(5,6),(7,8),(9,10)]<br>
[(4,9),(6,7)]<br>
[(4,7)]<br>
[(7,9)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N15L43D10"><td>Median selection network for 15 inputs, 43 CEs, 10 layers (cost=72):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(0,5),(1,11),(3,13),(6,12),(8,10),(9,14)]<br>
[(0,10),(1,12),(2,8),(3,11),(4,9),(5,7)]<br>
[(0,6),(1,10),(5,14),(7,13),(9,12)]<br>
[(1,4),(3,5),(6,8),(9,10),(11,14)]<br>
[(2,6),(4,8),(5,9),(7,10),(11,12)]<br>
[(3,4),(5,6),(7,11),(8,9)]<br>
[(4,6),(7,8)]<br>
[(6,7),(8,11)]<br>
[(7,8)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N15L46D9"><td>Median selection network for 15 inputs, 46 CEs, 9 layers (cost=78):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14)]<br>
[(0,12),(1,13),(2,14),(4,8),(5,9),(6,10),(7,11)]<br>
[(0,4),(1,6),(2,5),(3,8),(7,12),(9,14),(10,13)]<br>
[(1,4),(3,7),(5,6),(8,12),(9,10),(11,14)]<br>
[(2,4),(5,10),(6,7),(8,9),(11,13)]<br>
[(4,5),(6,8),(7,9),(10,11)]<br>
[(5,8),(7,10)]<br>
[(7,8)]<br>
</p>Constructed from N+1 case</td><td>
Auto generated</td></tr>
<tr id="N16L46D10"><td>Median selection network for 16 inputs, 46 CEs, 10 layers (cost=78):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15)]<br>
[(0,6),(2,4),(9,15),(11,13)]<br>
[(4,9),(6,11)]<br>
[(1,9),(3,11),(4,12),(6,14)]<br>
[(0,4),(1,10),(2,6),(3,8),(5,14),(7,12),(9,13),(11,15)]<br>
[(1,12),(3,14),(4,7),(5,6),(8,11),(9,10)]<br>
[(1,3),(4,5),(6,7),(8,9),(10,11),(12,14)]<br>
[(3,5),(6,8),(7,9),(10,12)]<br>
[(5,8),(7,10)]<br>
[(5,7),(8,10)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N16L47D9"><td>Median selection network for 16 inputs, 47 CEs, 9 layers (cost=80):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15)]<br>
[(0,6),(1,2),(3,5),(9,15),(10,12),(13,14)]<br>
[(0,8),(1,9),(2,12),(3,13),(4,10),(5,11),(6,14),(7,15)]<br>
[(1,10),(2,6),(3,8),(5,14),(7,12),(9,13)]<br>
[(5,7),(8,10)]<br>
[(2,8),(5,9),(6,10),(7,13)]<br>
[(5,8),(6,9),(7,10)]<br>
[(6,8),(7,9)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N16L49D8"><td>Median selection network for 16 inputs, 49 CEs, 8 layers (cost=84):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15)]<br>
[(0,12),(1,13),(2,14),(3,15),(4,8),(5,9),(6,10),(7,11)]<br>
[(0,4),(1,6),(2,5),(3,8),(7,12),(9,14),(10,13),(11,15)]<br>
[(1,4),(3,7),(5,6),(8,12),(9,10),(11,14)]<br>
[(2,4),(5,10),(6,7),(8,9),(11,13)]<br>
[(4,5),(6,8),(7,9),(10,11)]<br>
[(5,8),(7,10)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N17L49D12"><td>Median selection network for 17 inputs, 49 CEs, 12 layers (cost=82):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15)]<br>
[(1,2),(5,13),(6,14),(9,10)]<br>
[(1,9),(2,10),(5,6),(13,14)]<br>
[(1,4),(2,13),(5,8),(6,9),(7,10),(11,14)]<br>
[(2,6),(4,7),(8,11),(9,13)]<br>
[(0,8),(3,11),(4,12),(7,15),(9,16)]<br>
[(3,8),(6,9),(7,12)]<br>
[(3,7),(8,16)]<br>
[(7,9),(8,12)]<br>
[(6,7),(8,9)]<br>
[(7,8)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N17L51D11"><td>Median selection network for 17 inputs, 51 CEs, 11 layers (cost=86):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15)]<br>
[(1,4),(3,6),(5,8),(10,12),(11,13)]<br>
[(0,10),(1,12),(2,11),(3,13),(4,14),(8,15)]<br>
[(2,10),(3,12),(6,14),(8,9)]<br>
[(3,8),(4,10),(6,11),(9,12)]<br>
[(6,9),(7,10),(8,16)]<br>
[(6,8),(7,16)]<br>
[(7,8),(9,16)]<br>
[(8,9)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N17L56D10"><td>Median selection network for 17 inputs, 56 CEs, 10 layers (cost=96):<br>
<p class="mono">[(1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16)]<br>
[(1,3),(2,4),(5,7),(6,8),(9,11),(10,12),(13,15),(14,16)]<br>
[(1,5),(2,6),(3,7),(4,8),(9,13),(10,15),(11,14),(12,16)]<br>
[(0,3),(1,13),(2,10),(4,7),(5,11),(6,12),(8,9),(14,15)]<br>
[(0,13),(2,5),(3,6),(4,14),(7,15),(9,16),(10,11)]<br>
[(3,4),(5,10),(6,13),(7,11),(12,14)]<br>
[(3,8),(4,10),(6,7),(9,11),(12,13)]<br>
[(0,8),(4,6),(7,12),(9,10)]<br>
[(6,8),(7,9)]<br>
[(7,8)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N18L55D12"><td>Median selection network for 18 inputs, 55 CEs, 12 layers (cost=94):<br>
<p class="mono">[(1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16)]<br>
[(0,2),(3,5),(4,6),(7,9),(8,10),(11,13),(12,14),(15,17)]<br>
[(0,4),(1,5),(2,14),(3,15),(12,16),(13,17)]<br>
[(0,13),(1,12),(2,6),(4,17),(5,16),(11,15)]<br>
[(0,1),(4,12),(5,13),(16,17)]<br>
[(4,13),(5,9),(8,12)]<br>
[(4,7),(5,11),(6,12),(10,13)]<br>
[(1,11),(6,16),(7,8),(9,10)]<br>
[(2,8),(6,10),(7,11),(9,15)]<br>
[(2,9),(3,11),(6,14),(8,15)]<br>
[(6,9),(8,11)]<br>
[(6,8),(9,11)]<br>
</p>+ two-input averager (not displayed)<br>Constructed from N+2 case</td><td>
Auto generated</td></tr>
<tr id="N18L56D10"><td>Median selection network for 18 inputs, 56 CEs, 10 layers (cost=96):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17)]<br>
[(0,16),(1,17),(2,8),(4,6),(5,7),(9,15),(10,12),(11,13)]<br>
[(0,10),(1,12),(3,9),(5,16),(7,17),(8,14)]<br>
[(0,4),(1,5),(3,10),(7,14),(12,16),(13,17)]<br>
[(1,8),(2,5),(3,4),(6,10),(7,11),(9,16),(12,15),(13,14)]<br>
[(4,6),(5,9),(7,10),(8,12),(11,13)]<br>
[(2,4),(5,6),(7,8),(9,10),(11,12),(13,15)]<br>
[(4,8),(5,11),(6,12),(9,13)]<br>
[(6,9),(8,11)]<br>
[(6,8),(9,11)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N19L58D15"><td>Median selection network for 19 inputs, 58 CEs, 15 layers (cost=98):<br>
<p class="mono">[(1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16)]<br>
[(0,2),(3,5),(4,6),(7,9),(8,10),(11,13),(12,14),(15,17)]<br>
[(0,4),(1,5),(2,14),(3,15),(12,16),(13,17)]<br>
[(0,13),(1,12),(2,6),(4,17),(5,16),(11,15)]<br>
[(0,1),(4,12),(5,13),(16,17)]<br>
[(4,13),(5,9),(8,12)]<br>
[(4,7),(5,11),(6,12),(10,13)]<br>
[(1,11),(6,16),(7,8),(9,10)]<br>
[(2,8),(6,10),(7,11),(9,15)]<br>
[(2,9),(3,11),(6,14),(8,15)]<br>
[(6,9),(8,11)]<br>
[(6,8),(9,11)]<br>
[(8,9)]<br>
[(9,18)]<br>
[(8,9)]<br>
</p>Constructed from N-1 case</td><td>
Auto generated</td></tr>
<tr id="N19L59D12"><td>Median selection network for 19 inputs, 59 CEs, 12 layers (cost=100):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(1,8),(3,12),(5,10),(6,9),(11,14)]<br>
[(2,8),(3,5),(10,12),(11,13)]<br>
[(3,6),(4,8),(5,17),(7,11),(9,12),(10,16)]<br>
[(5,8),(6,10),(7,16),(9,17)]<br>
[(7,18),(8,10)]<br>
[(7,8),(9,10)]<br>
[(8,10),(9,18)]<br>
[(8,9)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N19L60D11"><td>Median selection network for 19 inputs, 60 CEs, 11 layers (cost=102):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(17,18)]<br>
[(1,12),(2,9),(3,13),(4,10),(5,14),(6,11)]<br>
[(1,4),(2,16),(3,10),(5,9),(6,12),(7,11),(13,15),(14,18)]<br>
[(0,4),(5,14),(6,17),(7,13),(10,12)]<br>
[(3,17),(4,5),(6,8),(7,14),(9,12),(10,16)]<br>
[(3,10),(5,8),(7,16),(9,17)]<br>
[(7,9),(8,10)]<br>
[(9,10)]<br>
[(7,9)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N20L63D12"><td>Median selection network for 20 inputs, 63 CEs, 12 layers (cost=108):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19)]<br>
[(1,5),(2,6),(3,15),(4,16),(13,17),(14,18)]<br>
[(1,14),(2,13),(3,7),(5,18),(6,17),(12,16)]<br>
[(0,16),(1,2),(3,19),(5,13),(6,14),(17,18)]<br>
[(0,4),(5,14),(6,10),(9,13),(15,19)]<br>
[(5,8),(6,12),(7,13),(11,14)]<br>
[(2,12),(7,17),(8,9),(10,11)]<br>
[(3,9),(7,11),(8,12),(10,16)]<br>
[(3,10),(4,12),(7,15),(9,16)]<br>
[(7,10),(9,12)]<br>
[(7,9),(10,12)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N20L64D10"><td>Median selection network for 20 inputs, 64 CEs, 10 layers (cost=110):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19)]<br>
[(0,4),(1,5),(2,6),(3,7),(12,16),(13,17),(14,18),(15,19)]<br>
[(0,12),(1,13),(2,14),(3,15),(4,16),(5,17),(6,18),(7,19)]<br>
[(1,4),(2,12),(3,8),(5,9),(6,13),(7,17),(10,14),(11,16),(15,18)]<br>
[(3,6),(4,10),(5,12),(7,14),(8,11),(9,15),(13,16)]<br>
[(6,8),(7,9),(10,12),(11,13)]<br>
[(6,10),(7,11),(8,12),(9,13)]<br>
[(8,10),(9,11)]<br>
[(7,10),(9,12)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N21L66D15"><td>Median selection network for 21 inputs, 66 CEs, 15 layers (cost=112):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19)]<br>
[(1,5),(2,6),(3,15),(4,16),(13,17),(14,18)]<br>
[(1,14),(2,13),(3,7),(5,18),(6,17),(12,16)]<br>
[(0,16),(1,2),(3,19),(5,13),(6,14),(17,18)]<br>
[(0,4),(5,14),(6,10),(9,13),(15,19)]<br>
[(5,8),(6,12),(7,13),(11,14)]<br>
[(2,12),(7,17),(8,9),(10,11)]<br>
[(3,9),(7,11),(8,12),(10,16)]<br>
[(3,10),(4,12),(7,15),(9,16)]<br>
[(7,10),(9,12)]<br>
[(7,9),(10,12)]<br>
[(9,10)]<br>
[(10,20)]<br>
[(9,10)]<br>
</p>Constructed from N-1 case</td><td>
Auto generated</td></tr>
<tr id="N21L67D12"><td>Median selection network for 21 inputs, 67 CEs, 12 layers (cost=114):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(17,18)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(1,2),(3,12),(4,8),(5,10),(6,9),(7,11),(13,14)]<br>
[(2,8),(3,19),(5,6),(9,10),(12,16)]<br>
[(3,5),(6,18),(7,19),(8,12),(9,17),(10,16)]<br>
[(5,9),(6,12),(7,17),(10,18)]<br>
[(7,13),(9,12),(10,20)]<br>
[(7,20),(10,12)]<br>
[(7,12),(9,10)]<br>
[(7,10)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N22L74D14"><td>Median selection network for 22 inputs, 74 CEs, 14 layers (cost=128):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,13),(14,16),(15,17),(18,20),(19,21)]<br>
[(0,4),(1,5),(2,6),(3,7),(14,18),(15,19),(16,20),(17,21)]<br>
[(0,14),(1,15),(2,16),(3,17),(4,18),(5,19),(6,20),(7,21)]<br>
[(3,8),(13,18)]<br>
[(3,14),(7,18),(8,11),(10,13)]<br>
[(1,10),(8,12),(9,13),(11,20)]<br>
[(4,8),(5,9),(6,10),(11,15),(12,16),(13,17)]<br>
[(2,5),(6,14),(7,15),(8,11),(10,13),(16,19)]<br>
[(5,11),(7,9),(10,16),(12,14)]<br>
[(5,8),(7,10),(11,14),(13,16)]<br>
[(7,11),(8,12),(9,13),(10,14)]<br>
[(9,11),(10,12)]<br>
[(9,10),(11,12)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N22L75D12"><td>Median selection network for 22 inputs, 75 CEs, 12 layers (cost=130):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,11),(9,12),(10,13),(14,16),(15,17),(18,20),(19,21)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,10),(11,13),(14,18),(15,19),(16,20),(17,21)]<br>
[(0,14),(1,15),(2,16),(3,17),(4,18),(5,19),(6,20),(7,21),(9,10),(11,12)]<br>
[(1,8),(3,18),(4,9),(6,11),(10,15),(12,17),(13,20)]<br>
[(2,10),(3,8),(6,14),(7,15),(11,19),(13,18)]<br>
[(5,10),(8,12),(9,13),(11,16)]<br>
[(5,9),(7,10),(11,14),(12,16)]<br>
[(3,11),(7,13),(8,14),(10,18)]<br>
[(7,8),(9,11),(10,12),(13,14)]<br>
[(8,10),(11,13)]<br>
[(8,11),(10,13)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N22L76D11"><td>Median selection network for 22 inputs, 76 CEs, 11 layers (cost=132):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,12),(9,13),(14,16),(15,17),(18,20),(19,21)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,11),(9,12),(10,13),(14,18),(15,19),(16,20),(17,21)]<br>
[(1,16),(2,15),(3,17),(4,18),(5,20),(6,19),(7,13),(8,14),(9,10),(11,12)]<br>
[(1,9),(4,7),(5,10),(11,16),(12,20),(14,17)]<br>
[(0,14),(2,5),(3,11),(6,9),(7,21),(10,18),(12,15),(16,19)]<br>
[(3,6),(5,12),(7,11),(9,16),(10,14),(15,18)]<br>
[(4,5),(6,10),(7,9),(11,15),(12,14),(16,17)]<br>
[(5,10),(7,12),(9,14),(11,16)]<br>
[(9,10),(11,12)]<br>
[(9,11),(10,12)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N23L77D16"><td>Median selection network for 23 inputs, 77 CEs, 16 layers (cost=132):<br>
<p class="mono">[(1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16),(17,18),(19,20)]<br>
[(0,2),(3,5),(4,6),(7,9),(8,10),(11,13),(12,14),(15,17),(16,18),(19,21)]<br>
[(0,4),(1,5),(2,6),(7,11),(8,12),(9,13),(10,14),(15,19),(16,20),(17,21)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(2,18),(4,20),(5,21),(8,16),(9,17),(11,19)]<br>
[(0,11),(1,8),(2,4),(3,9),(10,20),(12,21),(13,18),(17,19)]<br>
[(8,9),(10,12)]<br>
[(6,12),(9,11),(10,13)]<br>
[(4,10),(9,15),(11,17)]<br>
[(2,11),(8,15),(10,19),(13,17)]<br>
[(4,15),(5,11),(6,13),(10,16)]<br>
[(6,15),(10,22),(11,16)]<br>
[(6,10),(15,22)]<br>
[(5,10),(11,15)]<br>
[(10,11)]<br>
[(11,15)]<br>
</p>Constructed from N+2 case</td><td>
Auto generated</td></tr>
<tr id="N23L78D14"><td>Median selection network for 23 inputs, 78 CEs, 14 layers (cost=134):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(17,21),(18,22)]<br>
[(0,8),(2,16),(4,18),(5,22),(10,20),(12,17),(13,19),(14,21)]<br>
[(1,20),(2,12),(3,13),(4,10),(5,14),(6,17),(7,19),(9,16),(11,18),(15,22)]<br>
[(1,12),(9,10),(11,14),(13,18),(15,17),(16,20)]<br>
[(3,10),(5,11),(6,12),(13,21),(14,20)]<br>
[(3,8),(5,6),(7,14),(10,16),(11,12),(13,15)]<br>
[(6,10),(8,11),(12,16)]<br>
[(10,12),(11,13)]<br>
[(10,11),(12,13)]<br>
[(7,12)]<br>
[(7,11),(12,15)]<br>
[(11,12)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N23L80D13"><td>Median selection network for 23 inputs, 80 CEs, 13 layers (cost=138):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22)]<br>
[(0,20),(1,21),(2,22),(4,16),(5,13),(6,9),(7,19),(8,12),(10,18),(11,15),(14,17)]<br>
[(0,8),(1,16),(5,10),(6,14),(7,22),(9,17),(13,18),(15,19)]<br>
[(2,9),(3,10),(4,8),(7,12),(11,16),(13,20),(14,21)]<br>
[(1,7),(2,5),(3,14),(6,13),(9,20),(10,17),(11,12),(16,22),(18,21)]<br>
[(3,8),(5,7),(10,12),(11,13),(15,20),(16,18)]<br>
[(7,13),(8,11),(10,16),(12,15)]<br>
[(5,7),(9,13),(10,14),(16,18)]<br>
[(7,11),(9,10),(12,16),(13,14)]<br>
[(10,11),(12,13)]<br>
[(10,12),(11,13)]<br>
[(11,12)]<br>
</p>Constructed from N+1 case</td><td>
Auto generated</td></tr>
<tr id="N23L83D12"><td>Median selection network for 23 inputs, 83 CEs, 12 layers (cost=144):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22)]<br>
[(1,18),(2,20),(3,21),(4,17),(5,22),(6,19),(7,15),(8,16)]<br>
[(0,16),(3,11),(5,13),(6,14),(9,17),(10,18),(12,20)]<br>
[(1,12),(2,9),(3,5),(4,10),(6,16),(7,17),(11,22),(13,19),(14,21),(18,20)]<br>
[(3,12),(5,16),(7,18),(9,10),(11,20),(13,14)]<br>
[(5,7),(6,9),(10,12),(11,13),(14,17),(16,18)]<br>
[(3,10),(5,9),(7,12),(11,16),(13,20),(14,18)]<br>
[(7,11),(9,10),(12,16),(13,14)]<br>
[(10,11),(12,13)]<br>
[(11,12)]<br>
</p>Constructed from N+1 case</td><td>
Auto generated</td></tr>
<tr id="N24L82D14"><td>Median selection network for 24 inputs, 82 CEs, 14 layers (cost=142):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23)]<br>
[(0,8),(3,14),(5,13),(6,11),(9,20),(10,18),(12,17),(15,23)]<br>
[(1,17),(2,20),(3,21),(4,18),(5,19),(6,22),(7,15),(8,16)]<br>
[(1,9),(2,10),(3,5),(4,12),(11,19),(13,21),(14,22),(18,20)]<br>
[(9,10),(13,14)]<br>
[(10,12),(11,13)]<br>
[(6,12),(9,10),(11,17),(13,14)]<br>
[(5,12),(6,16),(7,17),(11,18)]<br>
[(3,16),(5,11),(7,20),(12,18)]<br>
[(7,11),(12,16)]<br>
[(7,10),(13,16)]<br>
[(10,12),(11,13)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N24L83D12"><td>Median selection network for 24 inputs, 83 CEs, 12 layers (cost=144):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23)]<br>
[(0,20),(1,21),(2,22),(3,23),(4,16),(5,13),(6,9),(7,19),(8,12),(10,18),(11,15),(14,17)]<br>
[(0,8),(1,16),(5,10),(6,14),(7,22),(9,17),(13,18),(15,23)]<br>
[(2,9),(3,10),(4,8),(7,12),(11,16),(13,20),(14,21),(15,19)]<br>
[(1,7),(2,5),(3,14),(6,13),(9,20),(10,17),(11,12),(16,22),(18,21)]<br>
[(3,8),(5,7),(10,12),(11,13),(15,20),(16,18)]<br>
[(7,13),(8,11),(10,16),(12,15)]<br>
[(5,7),(9,13),(10,14),(16,18)]<br>
[(7,11),(9,10),(12,16),(13,14)]<br>
[(10,11),(12,13)]<br>
[(10,12),(11,13)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N24L86D11"><td>Median selection network for 24 inputs, 86 CEs, 11 layers (cost=150):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23)]<br>
[(1,18),(2,20),(3,21),(4,17),(5,22),(6,19),(7,15),(8,16)]<br>
[(0,16),(3,11),(5,13),(6,14),(7,23),(9,17),(10,18),(12,20)]<br>
[(1,12),(2,9),(3,5),(4,10),(6,16),(7,17),(11,22),(13,19),(14,21),(18,20)]<br>
[(3,12),(5,16),(7,18),(9,10),(11,20),(13,14)]<br>
[(5,7),(6,9),(10,12),(11,13),(14,17),(16,18)]<br>
[(3,10),(5,9),(7,12),(11,16),(13,20),(14,18)]<br>
[(7,11),(9,10),(12,16),(13,14)]<br>
[(10,11),(12,13)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N25L85D16"><td>Median selection network for 25 inputs, 85 CEs, 16 layers (cost=146):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(3,19),(5,21),(6,22),(7,23),(8,16),(9,17),(10,18),(12,20)]<br>
[(1,12),(2,9),(3,5),(4,10),(11,21),(13,22),(14,19),(18,20)]<br>
[(9,10),(11,13)]<br>
[(7,13),(10,12),(11,14)]<br>
[(5,11),(10,16),(12,18)]<br>
[(3,12),(9,16),(11,20),(14,18)]<br>
[(5,16),(6,12),(7,14),(11,17)]<br>
[(7,16),(11,24),(12,17)]<br>
[(7,11),(16,24)]<br>
[(6,11),(12,16)]<br>
[(11,12)]<br>
[(12,16)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N25L86D15"><td>Median selection network for 25 inputs, 86 CEs, 15 layers (cost=148):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(2,16),(3,20),(5,22),(6,18),(9,21),(10,17),(12,19)]<br>
[(1,6),(3,8),(4,10),(5,12),(7,19),(9,18),(11,22),(14,21),(17,20)]<br>
[(6,10),(7,14),(9,24)]<br>
[(6,16),(7,11),(8,10),(14,23),(18,24)]<br>
[(7,12),(8,9),(10,17),(20,24)]<br>
[(5,10),(7,16),(12,20),(14,17)]<br>
[(10,18),(12,16),(13,14)]<br>
[(9,10),(11,13),(16,18)]<br>
[(10,12),(11,16)]<br>
[(11,12)]<br>
[(12,16)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N25L87D14"><td>Median selection network for 25 inputs, 87 CEs, 14 layers (cost=150):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(1,16),(3,20),(5,21),(6,19),(9,17),(10,18),(12,22)]<br>
[(2,9),(3,8),(4,10),(5,18),(6,12),(7,22),(11,21),(13,19),(17,20)]<br>
[(6,16),(7,13),(9,10),(12,17),(14,20),(18,24)]<br>
[(5,18),(7,11),(8,10),(14,23),(17,24)]<br>
[(7,14),(8,9),(10,16)]<br>
[(5,10),(7,17),(9,12),(13,14),(16,18)]<br>
[(7,16),(10,12),(11,18),(13,17)]<br>
[(11,13),(12,16)]<br>
[(11,16)]<br>
[(11,12)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N25L97D13"><td>Median selection network for 25 inputs, 97 CEs, 13 layers (cost=170):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,24)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(21,22),(23,24)]<br>
[(0,8),(1,12),(2,10),(3,14),(4,9),(5,13),(6,11),(7,15),(17,22),(18,21),(19,24)]<br>
[(1,16),(3,9),(4,18),(5,17),(6,20),(7,13),(11,14),(12,22),(15,24),(21,23)]<br>
[(2,18),(3,12),(4,16),(5,21),(6,8),(7,11),(10,17),(14,23),(19,20)]<br>
[(2,5),(7,18),(8,16),(9,21),(10,14),(12,19),(13,17),(20,22)]<br>
[(3,5),(7,9),(10,12),(11,20),(13,19),(14,21),(15,18)]<br>
[(5,9),(8,10),(11,15),(12,16),(13,14)]<br>
[(5,10),(7,12),(9,16),(11,13),(14,19)]<br>
[(9,12),(10,11),(13,15),(14,16)]<br>
[(11,12),(13,14)]<br>
[(12,13)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N26L94D16"><td>Median selection network for 26 inputs, 94 CEs, 16 layers (cost=164):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(14,16),(15,17),(18,20),(19,21),(22,24),(23,25)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,14),(9,15),(10,16),(11,17),(18,22),(19,23),(20,24),(21,25)]<br>
[(0,18),(1,19),(2,20),(3,21),(4,22),(5,23),(6,24),(7,25),(9,16)]<br>
[(2,8),(3,13),(6,9),(12,22),(16,19),(17,23)]<br>
[(1,6),(9,13),(12,16),(19,24)]<br>
[(8,12),(9,14),(11,16),(13,17)]<br>
[(6,12),(7,16),(9,18),(13,19)]<br>
[(3,18),(7,22),(11,12),(13,14)]<br>
[(3,11),(7,15),(10,18),(14,22)]<br>
[(4,10),(5,18),(6,11),(7,20),(14,19),(15,21)]<br>
[(5,13),(10,11),(12,20),(14,15)]<br>
[(5,10),(11,13),(12,14),(15,20)]<br>
[(7,11),(10,12),(13,15),(14,18)]<br>
[(11,13),(12,14)]<br>
[(11,12),(13,14)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N26L95D14"><td>Median selection network for 26 inputs, 95 CEs, 14 layers (cost=166):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(14,16),(15,17),(18,20),(19,21),(22,24),(23,25)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,14),(9,15),(10,16),(11,17),(18,22),(19,23),(20,24),(21,25)]<br>
[(0,18),(1,19),(2,20),(3,21),(4,22),(5,23),(6,24),(7,25),(9,10),(15,16)]<br>
[(1,4),(2,18),(3,22),(5,20),(6,19),(7,23),(10,14),(11,15),(21,24)]<br>
[(3,6),(4,21),(5,12),(7,17),(8,18),(13,20),(19,22)]<br>
[(1,5),(3,9),(4,10),(6,14),(11,19),(15,21),(16,22),(20,24)]<br>
[(2,5),(7,15),(9,12),(10,18),(13,16),(20,23)]<br>
[(5,11),(10,13),(12,15),(14,20)]<br>
[(11,13),(12,14)]<br>
[(6,11),(7,13),(12,18),(14,19)]<br>
[(7,9),(11,12),(13,14),(16,18)]<br>
[(9,12),(13,16)]<br>
[(9,13),(12,16)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N26L96D13"><td>Median selection network for 26 inputs, 96 CEs, 13 layers (cost=168):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(14,16),(15,17),(18,20),(19,21),(22,24),(23,25)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,14),(9,15),(10,16),(11,17),(18,22),(19,23),(20,24),(21,25)]<br>
[(0,18),(1,19),(2,20),(3,21),(4,22),(5,23),(6,24),(7,25),(10,14),(11,15)]<br>
[(1,4),(2,18),(3,22),(5,20),(6,19),(7,23),(21,24)]<br>
[(2,8),(3,6),(4,18),(5,12),(7,21),(13,20),(17,23),(19,22)]<br>
[(3,10),(5,8),(6,14),(7,16),(9,18),(11,19),(15,22),(17,20)]<br>
[(4,11),(6,8),(7,13),(9,10),(12,18),(14,21),(15,16),(17,19)]<br>
[(1,7),(8,11),(10,13),(12,15),(14,17),(18,24)]<br>
[(7,11),(10,12),(13,15),(14,18)]<br>
[(7,12),(8,14),(11,17),(13,18)]<br>
[(11,13),(12,14)]<br>
[(11,12),(13,14)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N26L104D12"><td>Median selection network for 26 inputs, 104 CEs, 12 layers (cost=184):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(14,16),(15,17),(18,20),(19,21),(22,24),(23,25)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,14),(9,16),(10,13),(11,17),(12,15),(18,22),(19,23),(20,24),(21,25)]<br>
[(0,18),(1,19),(2,20),(3,21),(4,22),(5,23),(6,24),(7,25),(9,12),(10,14),(11,15),(13,16)]<br>
[(1,18),(2,10),(3,20),(4,8),(5,22),(6,14),(7,24),(11,19),(15,23),(17,21)]<br>
[(1,9),(3,13),(5,15),(6,18),(7,19),(8,11),(10,20),(12,22),(14,17),(16,24)]<br>
[(2,6),(3,11),(5,9),(7,17),(8,18),(10,12),(13,15),(14,22),(16,20),(19,23)]<br>
[(3,10),(7,13),(9,14),(11,16),(12,18),(15,22)]<br>
[(5,10),(7,12),(9,11),(13,18),(14,16),(15,20)]<br>
[(6,10),(7,9),(11,13),(12,14),(15,19),(16,18)]<br>
[(9,10),(11,12),(13,14),(15,16)]<br>
[(10,12),(13,15)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N27L97D18"><td>Median selection network for 27 inputs, 97 CEs, 18 layers (cost=168):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23),(24,26)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23),(25,26)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15)]<br>
[(5,21),(6,20),(8,16),(9,19),(10,18),(13,23)]<br>
[(5,24),(9,25),(19,20)]<br>
[(5,16),(9,12),(11,20),(13,19),(18,25)]<br>
[(6,9),(11,19),(13,26),(16,24),(17,18),(21,25)]<br>
[(4,17),(9,13),(10,16),(11,26),(12,18),(21,24)]<br>
[(3,13),(7,18),(12,17),(14,24),(16,21)]<br>
[(1,12),(2,16),(7,22),(17,21)]<br>
[(3,17),(7,16),(9,12),(11,22),(13,21)]<br>
[(7,12),(11,16)]<br>
[(11,14),(12,17)]<br>
[(11,12),(14,16)]<br>
[(12,13),(14,17)]<br>
[(13,14)]<br>
[(12,13)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N27L98D17"><td>Median selection network for 27 inputs, 98 CEs, 17 layers (cost=170):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(14,16),(15,17),(18,20),(19,21),(22,24),(23,25)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,14),(9,15),(10,16),(11,17),(18,22),(19,23),(20,24),(21,25)]<br>
[(0,18),(1,19),(2,20),(3,21),(4,22),(5,23),(6,24),(7,25),(9,10),(15,16)]<br>
[(1,4),(2,18),(3,22),(5,20),(6,19),(7,23),(10,14),(11,15),(21,24)]<br>
[(3,6),(4,21),(5,12),(7,17),(8,18),(13,20),(19,22)]<br>
[(1,5),(3,9),(4,10),(6,14),(11,19),(15,21),(16,22),(20,24)]<br>
[(2,5),(7,15),(9,12),(10,18),(13,16),(20,23)]<br>
[(5,11),(10,13),(12,15),(14,20)]<br>
[(11,13),(12,14)]<br>
[(6,11),(7,13),(12,18),(14,19)]<br>
[(7,9),(11,12),(13,14),(16,18)]<br>
[(9,12),(13,16)]<br>
[(9,13),(12,16)]<br>
[(12,13)]<br>
[(13,26)]<br>
[(12,13)]<br>
</p>Constructed from N-1 case</td><td>
Auto generated</td></tr>
<tr id="N27L99D15"><td>Median selection network for 27 inputs, 99 CEs, 15 layers (cost=172):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23),(25,26)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,12),(9,13),(10,14),(11,15),(16,20),(17,21),(18,22),(19,23)]<br>
[(0,8),(1,9),(2,10),(3,11),(4,12),(5,13),(6,14),(7,15),(19,26),(20,25)]<br>
[(1,2),(3,12),(4,8),(5,10),(6,9),(7,11),(13,14),(18,20)]<br>
[(2,8),(3,24),(4,18),(5,20),(6,17),(7,26),(9,21),(10,22),(12,19),(13,23)]<br>
[(2,12),(3,6),(7,13),(8,16),(10,17),(11,21),(14,22)]<br>
[(6,18),(7,25),(10,12),(13,20),(14,19),(17,24)]<br>
[(7,16),(9,17),(10,18),(11,25),(14,24)]<br>
[(1,7),(5,9),(11,14),(13,18),(16,17)]<br>
[(7,13),(11,12),(16,18)]<br>
[(9,13),(11,16),(17,18)]<br>
[(12,17),(13,16)]<br>
[(12,13)]<br>
[(13,16)]<br>
</p></td><td>
Auto generated</td></tr>
<tr id="N27L102D14"><td>Median selection network for 27 inputs, 102 CEs, 14 layers (cost=178):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23),(24,26)]<br>
[(0,16),(1,25),(2,26),(3,11),(4,12),(5,9),(6,10),(7,19),(8,20),(15,23),(17,21),(18,22)]<br>
[(1,13),(5,18),(6,17),(7,15),(9,22),(10,21),(11,19),(12,20),(14,26),(16,24)]<br>
[(0,12),(1,10),(2,9),(3,7),(6,14),(8,16),(11,23),(13,21),(17,26),(18,25),(20,24)]<br>
[(1,5),(3,20),(4,16),(6,12),(7,24),(10,14),(13,17),(15,21),(22,26)]<br>
[(2,13),(7,11),(9,17),(10,18),(14,25),(16,20)]<br>
[(3,16),(5,13),(7,15),(9,18),(11,24),(12,20),(14,22)]<br>
[(2,16),(5,12),(7,9),(11,25),(15,22),(18,20)]<br>
[(7,16),(9,13),(10,12),(11,20),(14,18),(15,17)]<br>
[(9,14),(11,15),(12,16),(13,18)]<br>
[(11,13),(14,16)]<br>
[(11,14),(13,16)]<br>
[(13,14)]<br>
</p>Constructed from N+1 case</td><td>
Auto generated</td></tr>
<tr id="N28L104D14"><td>Median selection network for 28 inputs, 104 CEs, 14 layers (cost=182):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25),(26,27)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23),(24,26),(25,27)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,16),(9,17),(10,18),(11,19),(20,24),(21,25),(22,26),(23,27)]<br>
[(0,20),(1,21),(2,22),(3,23),(4,24),(5,25),(6,26),(7,27),(10,12),(15,17)]<br>
[(1,10),(3,13),(6,15),(7,19),(8,20),(12,21),(14,24),(17,26)]<br>
[(3,16),(7,17),(9,14),(10,20),(11,24),(13,18)]<br>
[(2,9),(3,4),(5,16),(6,20),(7,21),(10,12),(11,22),(15,17),(18,25),(23,24)]<br>
[(4,9),(5,11),(6,12),(7,14),(13,20),(15,21),(16,22),(18,23)]<br>
[(4,7),(5,13),(9,12),(14,22),(15,18),(20,23)]<br>
[(9,13),(11,12),(14,18),(15,16)]<br>
[(7,13),(11,15),(12,16),(14,20)]<br>
[(7,15),(11,14),(12,20),(13,16)]<br>
[(12,13),(14,15)]<br>
[(12,14),(13,15)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N28L105D13"><td>Median selection network for 28 inputs, 105 CEs, 13 layers (cost=184):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25),(26,27)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23),(24,26),(25,27)]<br>
[(0,16),(1,25),(2,26),(4,12),(5,9),(6,10),(7,19),(8,20),(11,27),(15,23),(17,21),(18,22)]<br>
[(1,13),(3,11),(5,18),(6,17),(7,15),(9,22),(10,21),(12,20),(14,26),(16,24)]<br>
[(0,12),(1,10),(2,9),(3,7),(6,14),(8,16),(11,19),(13,21),(15,27),(17,26),(18,25),(20,24)]<br>
[(1,5),(3,20),(4,16),(6,12),(7,24),(10,14),(11,23),(13,17),(15,21),(22,26)]<br>
[(2,13),(7,11),(9,17),(10,18),(14,25),(16,20)]<br>
[(3,16),(5,13),(7,15),(9,18),(11,24),(12,20),(14,22)]<br>
[(2,16),(5,12),(7,9),(11,25),(15,22),(18,20)]<br>
[(7,16),(9,13),(10,12),(11,20),(14,18),(15,17)]<br>
[(9,14),(11,15),(12,16),(13,18)]<br>
[(11,13),(14,16)]<br>
[(11,14),(13,16)]<br>
</p>+ two-input averager (not displayed)<br></td><td>
Auto generated</td></tr>
<tr id="N29L107D17"><td>Median selection network for 29 inputs, 107 CEs, 17 layers (cost=186):<br>
<p class="mono">[(0,1),(2,3),(4,5),(6,7),(8,9),(10,11),(12,13),(14,15),(16,17),(18,19),(20,21),(22,23),(24,25),(26,27)]<br>
[(0,2),(1,3),(4,6),(5,7),(8,10),(9,11),(12,14),(13,15),(16,18),(17,19),(20,22),(21,23),(24,26),(25,27)]<br>
[(0,4),(1,5),(2,6),(3,7),(8,16),(9,17),(10,18),(11,19),(20,24),(21,25),(22,26),(23,27)]<br>
[(0,20),(1,21),(2,22),(3,23),(4,24),(5,25),(6,26),(7,27),(10,12),(15,17)]<br>
[(1,10),(3,13),(6,15),(7,19),(8,20),(12,21),(14,24),(17,26)]<br>
[(3,16),(7,17),(9,14),(10,20),(11,24),(13,18)]<br>
[(2,9),(3,4),(5,16),(6,20),(7,21),(10,12),(11,22),(15,17),(18,25),(23,24)]<br>
[(4,9),(5,11),(6,12),(7,14),(13,20),(15,21),(16,22),(18,23)]<br>
[(4,7),(5,13),(9,12),(14,22),(15,18),(20,23)]<br>
[(9,13),(11,12),(14,18),(15,16)]<br>
[(7,13),(11,15),(12,16),(14,20)]<br>
[(7,15),(11,14),(12,20),(13,16)]<br>
[(12,13),(14,15)]<br>
[(12,14),(13,15)]<br>
[(13,14)]<br>
[(14,28)]<br>
[(13,14)]<br>
</p>Constructed from N-1 case</td><td>
Auto generated</td></tr>