pymor · github-actions · Jan 11, 2024
diff --git a/badge.json b/badge.json
@@ -1 +1 @@
-{"schemaVersion": 1, "label": "docs", "message": "2023.2.0", "color": "green"}
+{"schemaVersion": 1, "label": "docs", "message": "2023.2.0", "color": "green"}
diff --git a/latest.tag b/latest.tag
@@ -1 +1 @@
-2023.1.1
+2023.2.0
diff --git a/latest/.buildinfo b/latest/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: f34393d660beb5d3f17e72279103c3af
+config: 9cff14370f0355e40d1c69f80af0ee57
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/latest/_downloads/049752ef291d62bd7bcde4a95068666e/tutorial_bt.ipynb b/latest/_downloads/049752ef291d62bd7bcde4a95068666e/tutorial_bt.ipynb
diff --git a/latest/_downloads/0747cc4a268a0cc5652b64e85e6124d0/tutorial_basis_generation.md b/latest/_downloads/0747cc4a268a0cc5652b64e85e6124d0/tutorial_basis_generation.md
@@ -129,7 +129,7 @@ parameter_space = fom.parameters.space(0.0001, 1.)
 
 Here, `0.0001` and `1.` are the common lower and upper bounds for the
 individual components of all |parameters| of `fom`. In our case `fom`
-expects a single parameter `'diffusion'` of 9 values:
+expects a single parameter `'diffusion'` of 6 values:
 
 ```{code-cell}
 fom.parameters
@@ -272,7 +272,7 @@ which determines {math}`\lambda_i` and, thus, {math}`v_{proj}`.
 
 Let's assemble and solve this equation system using pyMOR to determine
 the best-approximation error in `trivial_basis` for some test vector
-`V` which we take as another random solution of our {{ Model }}:
+`U` which we take as another random solution of our {{ Model }}:
 
 ```{code-cell}
 U = fom.solve(parameter_space.sample_randomly())
@@ -328,7 +328,7 @@ the same length can simply be subtracted, yielding a new array of the
 differences:
 
 ```{code-cell}
-# for some reason V_proj does not carry over from the previous cell
+# for some reason U_proj does not carry over from the previous cell
 U_proj = trivial_basis.lincomb(lambdas.T)
 fom.visualize((U, U_proj, U - U_proj),
  legend=('U', 'U_proj', 'best-approximation err'),

diff --git a/latest/_downloads/1000b3f568829899be8a4f7a0b7f7e9a/tutorial_optimization.ipynb b/latest/_downloads/1000b3f568829899be8a4f7a0b7f7e9a/tutorial_optimization.ipynb