Merge remote-tracking branch 'origin/develop' into uri/optimizer_result

docs/notebooks/active_learning.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Active Learning
+# # Active learning
 
 # %% [markdown]
 # Sometimes, we may just want to learn a black-box function, rather than optimizing it. This goal is known as active learning and corresponds to choosing query points that reduce our model uncertainty. This notebook demonstrates how to perform Bayesian active learning using Trieste.

docs/notebooks/active_learning_for_binary_classification.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Active Learning for binary classification
+# # Active learning for binary classification
 
 # %%
 import gpflow

docs/notebooks/ask_tell_optimization.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Ask-Tell Optimization Interface
+# # Ask-Tell optimization interface
 
 # %% [markdown]
 # In this notebook we will illustrate the use of an Ask-Tell interface in Trieste. It is useful for cases where you want to have greater control of the optimization loop, or when letting Trieste manage this loop is impossible.

docs/notebooks/batch_optimization.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Batch Bayesian Optimization
+# # Batch Bayesian optimization
 
 # %% [markdown]
 # Sometimes it is practically convenient to query several points at a time. This notebook demonstrates four ways to perfom batch Bayesian optimization with Trieste.

docs/notebooks/expected_improvement.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Introduction to Bayesian Optimization
+# # Introduction to Bayesian optimization
 
 # %%
 import numpy as np

docs/notebooks/multifidelity_modelling.pct.py

@@ -16,7 +16,7 @@
 import gpflow.kernels
 
 # %% [markdown]
-# # Multifidelity Modelling
+# # Multifidelity modelling
 #
 # This tutorial demonstrates the usage of the `MultifidelityAutoregressive` model for fitting multifidelity data. This is an implementation of the AR1 model initially described in <cite data-cite="Kennedy2000"/>.
 

docs/notebooks/rembo.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # High-dimensional Bayesian Optimization
+# # High-dimensional Bayesian optimization
 # This notebook demonstrates a simple method for optimizing a high-dimensional (100-D) problem, where standard BO methods have trouble.
 
 # %%

docs/notebooks/scalable_thompson_sampling_using_sparse_gaussian_processes.pct.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 # %% [markdown]
-# # Scalable Thompson Sampling
+# # Scalable Thompson sampling
 
 # %% [markdown]
 # In our other [Thompson sampling notebook](thompson_sampling.pct.py) we demonstrate how to perform batch optimization using a traditional implementation of Thompson sampling that samples exactly from an underlying Gaussian Process surrogate model. Unfortunately, this approach incurs a large computational overhead that scales polynomially with the optimization budget and so cannot be applied to settings with larger optimization budgets, e.g. those where large batches (>>10) of points can be collected.

docs/notebooks/thompson_sampling.pct.py

@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Thompson Sampling
+# # Thompson sampling
 
 # %%
 import numpy as np

docs/tutorials.rst

@@ -15,10 +15,10 @@
 Tutorials
 =========
 
-Example optimization problems
------------------------------
+Optimization problems
+---------------------
 
-The following tutorials explore various types of optimization problems using Trieste.
+The following tutorials illustrate solving different types of optimization problems using Trieste.
 
 .. toctree::
    :maxdepth: 1
@@ -44,7 +44,7 @@ The following tutorials explore various types of optimization problems using Tri
 Frequently asked questions
 --------------------------
 
-The following tutorials (or sections thereof) explain how to use and extend specific Trieste functionality.
+The following tutorials explain how to use and extend specific Trieste functionality.
 
 * :doc:`How do I set up a basic Bayesian optimization routine?<notebooks/expected_improvement>`
 * :doc:`How do I set up a batch Bayesian optimization routine?<notebooks/batch_optimization>`