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     <title>Problems - MIPLearn</title>
     
 
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@@ -154,6 +152,10 @@
                 <li class="third-level"><a href="#problem-definition_1">Problem definition</a></li>
                 <li class="third-level"><a href="#random-instance-generator_1">Random instance generator</a></li>
                 <li class="third-level"><a href="#challenge-a_1">Challenge A</a></li>
+            <li class="second-level"><a href="#traveling-salesman-problem">Traveling Salesman Problem</a></li>
+                
+                <li class="third-level"><a href="#problem-definition_2">Problem definition</a></li>
+                <li class="third-level"><a href="#random-problem-generator">Random problem generator</a></li>
     </ul>
 </div></div>
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@@ -251,7 +253,26 @@ from the provided probability distributions <code>K</code> and <code>u</code>.</
                       )
 </code></pre>
 
-<p><img alt="alt" src="../figures/benchmark_knapsack_a.png" /></p></div>
+<p><img alt="alt" src="../figures/benchmark_knapsack_a.png" /></p>
+<h2 id="traveling-salesman-problem">Traveling Salesman Problem</h2>
+<h3 id="problem-definition_2">Problem definition</h3>
+<p>Given a list of cities and the distance between each pair of cities, the problem asks for the
+shortest route starting at the first city, visiting each other city exactly once, then returning
+to the first city. This problem is a generalization of the Hamiltonian path problem, one of Karp's
+21 NP-complete problems.</p>
+<h3 id="random-problem-generator">Random problem generator</h3>
+<p>The class <code>TravelingSalesmanGenerator</code> can be used to generate random instances of this
+problem. Initially, the generator creates $n$ cities $(x_1,y_1),\ldots,(x_n,y_n) \in \mathbb{R}^2$,
+where $n, x_i$ and $y_i$ are sampled independently from the provided probability distributions <code>n</code>,
+<code>x</code> and <code>y</code>. For each pair of cities $(i,j)$, the distance $d_{i,j}$ between them is set to:
+<script type="math/tex; mode=display">
+    d_{i,j} = \gamma_{i,j} \sqrt{(x_i-x_j)^2 + (y_i - y_j)^2}
+</script>
+where $\gamma_{i,j}$ is sampled from the distribution <code>gamma</code>.</p>
+<p>If <code>fix_cities=True</code> is provided, the list of cities is kept the same for all generated instances.
+The $gamma$ values, and therefore also the distances, are still different.</p>
+<p>By default, all distances $d_{i,j}$ are rounded to the nearest integer.  If <code>round=False</code>
+is provided, this rounding will be disabled.</p></div>
         
         
     </div>
@@ -269,7 +290,8 @@ from the provided probability distributions <code>K</code> and <code>u</code>.</
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@@ -281,11 +303,8 @@ from the provided probability distributions <code>K</code> and <code>u</code>.</
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