move docs page out of examples subfolder

CliMA · May 24, 2023 · cb6e42f · cb6e42f
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Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/docs/make.jl b/docs/make.jl
@@ -53,13 +53,13 @@ api = [
     "Unscented" => "API/Unscented.md",
     "Sampler" => "API/Sampler.md",
     "SparseInversion" => "API/SparseInversion.md",
+    "Learning rate schedulers" => "learning_rate_scheduler.md",
     "TOML Interface" => "API/TOMLInterface.md",
     "Localizers" => "API/Localizers.md",
 ]
 
 examples = [
     "Simple example" => "literated/sinusoid_example.md",
-    "Learning rate schedulers" => "examples/learning_rate_scheduler.md",
     "Cloudy" => "examples/Cloudy_example.md",
     "Lorenz" => "examples/lorenz_example.md",
     "Minimization Loss" => "literated/loss_minimization.md",

diff --git a/docs/src/examples/learning_rate_scheduler.md → docs/src/learning_rate_scheduler.md b/docs/src/examples/learning_rate_scheduler.md → docs/src/learning_rate_scheduler.md
@@ -35,7 +35,7 @@ There are two termination times that the theory indicates are useful
 The experiment assesses the schedulers robustly by solving the inverse problem 100 times with different random seeds.
 Shown below is an example plot of one of these solves with each timestepper.
 
-![Solution ensembles](../assets/ensemble_compare_scheduler.png)
+![Solution ensembles](assets/ensemble_compare_scheduler.png)
 
 Left: The true model over ``[0,2\pi]`` (black), and the ensembles from different schemes (colors).
 Right: The noisy observation (black), the distribution it was sampled from (gray-ribbon), and the corresponding ensemble-mean approximation given from each scheduler (colors).
@@ -44,7 +44,7 @@ To assess the timestepping we show the convergence plot against the algorithm it
 - error (solid) is defined by ``\frac{1}{N_{ens}}\sum^{N_{ens}}_{i=1} \| \theta_i - \theta^* \|^2`` where ``\theta_i`` are ensemble members and ``\theta^*`` is the true value used to create the observed data.
 - spread (dashed) is defined by ``\frac{1}{N_{ens}}\sum^{N_{ens}}_{i=1} \| \theta_i - \bar{\theta} \|^2`` where ``\theta_i`` are ensemble members and ``\bar{\theta}`` is the mean over these members.
 
-![Error vs spread](../assets/error_vs_spread_over_iteration_compare_scheduler.png)
+![Error vs spread](assets/error_vs_spread_over_iteration_compare_scheduler.png)
 
 Left: the error and spread of the different timesteppers at over iterations of the algorithm for a single run.
 Right: the error and spread of the different timesteppers at their final iterations, averaged from 100 different draws of initial conditions