mtezzele · github-actions · Mar 8, 2024
diff --git a/docs/source/_tutorials/tutorial10paramdmd.html b/docs/source/_tutorials/tutorial10paramdmd.html
diff --git a/docs/source/_tutorials/tutorial12cdmd.html b/docs/source/_tutorials/tutorial12cdmd.html
diff --git a/docs/source/_tutorials/tutorial14bopdmd.html b/docs/source/_tutorials/tutorial14bopdmd.html
diff --git a/docs/source/_tutorials/tutorial15pidmd.html b/docs/source/_tutorials/tutorial15pidmd.html
diff --git a/docs/source/_tutorials/tutorial16rdmd.html b/docs/source/_tutorials/tutorial16rdmd.html
diff --git a/docs/source/_tutorials/tutorial18lando.html b/docs/source/_tutorials/tutorial18lando.html
diff --git a/docs/source/_tutorials/tutorial19havok.html b/docs/source/_tutorials/tutorial19havok.html
@@ -7712,7 +7712,7 @@ <h3 id="Basic-HAVOK-application">Basic HAVOK application<a class="anchor-link" h
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;pydmd.havok.HAVOK at 0x7fbe35b75b20&gt;</pre>
+<pre>&lt;pydmd.havok.HAVOK at 0x7f92dc56e220&gt;</pre>
 </div>
 </div>
 </div>

diff --git a/docs/source/_tutorials/tutorial1dmd.html b/docs/source/_tutorials/tutorial1dmd.html
diff --git a/tutorials/tutorial10/tutorial-10-paramdmd.py b/tutorials/tutorial10/tutorial-10-paramdmd.py
@@ -3,7 +3,7 @@
 
 # # Parametric Dynamic Mode Decomposition
 #
-# In this tutorial we explore the usage of the class `pydmd.ParametricDMD`, presented in *A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems* by Andreuzzi et all ( [doi](https://doi.org/10.1137/22M1481658) ). The approach provides an extension Dynamic Mode Decomposition to parametric problems, in order to obtain predictions for future time instants in untested parameters.
+# In this tutorial we explore the usage of the class `pydmd.ParametricDMD`, presented in *A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems* by Andreuzzi et all ([doi](https://doi.org/10.1137/22M1481658) ). The approach provides an extension Dynamic Mode Decomposition to parametric problems, in order to obtain predictions for future time instants in untested parameters.
 #
 # We'll examine a simple parametric time-dependent problem, the sum of two complex period functions:
 # $$\begin{cases}

diff --git a/tutorials/tutorial14/tutorial-14-bop-dmd.py b/tutorials/tutorial14/tutorial-14-bop-dmd.py
@@ -116,7 +116,7 @@ def relative_error(x_est, x_true):
 
 
 optdmd = BOPDMD(svd_rank=1, num_trials=0)
-bopdmd = BOPDMD(svd_rank=1, num_trials=100)
+bopdmd = BOPDMD(svd_rank=1, num_trials=100, varpro_opts_dict={"tol": 0.0115})
 dmd = DMD(svd_rank=1, tlsq_rank=2, exact=True, opt=True)
 
 optdmd.fit(snapshots_1d, time)