diff --git a/README.rst b/README.rst
index 3bf7c4f..3bdb025 100644
--- a/README.rst
+++ b/README.rst
@@ -38,7 +38,7 @@ If you use pyblp in your research, we ask that you also cite `Conlon and Gortmak
 Installation
 ------------
 
-The pyblp package has been tested on `Python <https://www.python.org/downloads/>`_ versions 3.6 and 3.7. The `SciPy instructions <https://scipy.org/install.html>`_ for installing related packages is a good guide for how to install a scientific Python environment. A good choice is the `Anaconda Distribution <https://www.anaconda.com/distribution/>`_, since, along with many other packages that are useful for scientific computing, it comes packaged with pyblp's only required dependencies: `NumPy <https://www.numpy.org/>`_, `SciPy <https://www.scipy.org/>`_, `SymPy <https://www.sympy.org/en/index.html>`_, and `Patsy <https://patsy.readthedocs.io/en/latest/>`_.
+The pyblp package has been tested on `Python <https://www.python.org/downloads/>`_ versions 3.6 and 3.7. The `SciPy instructions <https://scipy.org/install.html>`_ for installing related packages is a good guide for how to install a scientific Python environment. A good choice is the `Anaconda Distribution <https://www.anaconda.com/distribution/>`_, since, along with many other packages that are useful for scientific computing, it comes packaged with pyblp's only required dependencies: `NumPy <https://numpy.org/>`_, `SciPy <https://www.scipy.org/>`_, `SymPy <https://www.sympy.org/en/index.html>`_, and `Patsy <https://patsy.readthedocs.io/en/latest/>`_.
 
 However, pyblp may not work with old versions of its dependencies. You can update pyblp's dependencies in Anaconda with::
 
diff --git a/docs/notebooks/api/data.ipynb b/docs/notebooks/api/data.ipynb
index 47de1ae..810ebad 100644
--- a/docs/notebooks/api/data.ipynb
+++ b/docs/notebooks/api/data.ipynb
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Any number of functions can be used to load the example data into memory. In this example, we'll first use [NumPy](https://www.numpy.org/)."
+    "Any number of functions can be used to load the example data into memory. In this example, we'll first use [NumPy](https://numpy.org/)."
    ]
   },
   {
diff --git a/docs/notebooks/tutorial/logit_nested.ipynb b/docs/notebooks/tutorial/logit_nested.ipynb
index bf7d2fc..e0d3847 100644
--- a/docs/notebooks/tutorial/logit_nested.ipynb
+++ b/docs/notebooks/tutorial/logit_nested.ipynb
@@ -103,7 +103,7 @@
    "source": [
     "### Loading the Data\n",
     "\n",
-    "The `product_data` argument of :class:`Problem` should be a structured array-like object with fields that store data. Product data can be a structured [NumPy](https://www.numpy.org/) array, a [pandas](https://pandas.pydata.org/) DataFrame, or other similar objects."
+    "The `product_data` argument of :class:`Problem` should be a structured array-like object with fields that store data. Product data can be a structured [NumPy](https://numpy.org/) array, a [pandas](https://pandas.pydata.org/) DataFrame, or other similar objects."
    ]
   },
   {
diff --git a/docs/notebooks/tutorial/nevo.ipynb b/docs/notebooks/tutorial/nevo.ipynb
index bfa3e66..63f07ef 100644
--- a/docs/notebooks/tutorial/nevo.ipynb
+++ b/docs/notebooks/tutorial/nevo.ipynb
@@ -135,7 +135,7 @@
    "source": [
     "### Loading the Data\n",
     "\n",
-    "The `product_data` argument of :class:`Problem` should be a structured array-like object with fields that store data. Product data can be a structured [NumPy](https://www.numpy.org/) array, a [pandas](https://pandas.pydata.org/) DataFrame, or other similar objects."
+    "The `product_data` argument of :class:`Problem` should be a structured array-like object with fields that store data. Product data can be a structured [NumPy](https://numpy.org/) array, a [pandas](https://pandas.pydata.org/) DataFrame, or other similar objects."
    ]
   },
   {
diff --git a/docs/testing.rst b/docs/testing.rst
index 6d99854..7c5acea 100644
--- a/docs/testing.rst
+++ b/docs/testing.rst
@@ -11,7 +11,7 @@ In addition to the installation requirements for the package itself, running tes
 
 The full suite of tests also requires installation of the following software:
 
-- `Artleys Knitro <https://www.artelys.com/en/optimization-tools/knitro>`_ version 10.3 or newer: testing optimization routines.
+- `Artleys Knitro <https://www.artelys.com/solvers/knitro/>`_ version 10.3 or newer: testing optimization routines.
 - `MATLAB <https://www.mathworks.com/products/matlab.html>`_: comparing sparse grids with those created by the function `nwspgr <http://www.sparse-grids.de/>`_ created by Florian Heiss and Viktor Winschel, which must be included in a directory on the MATLAB path.
 
 If software is not installed, its associated tests will be skipped. Additionally, some tests that require support for extended precision will be skipped if on the platform running the tests, ``numpy.longdouble`` has the same precision as ``numpy.float64``. This tends to be the case on Windows.
diff --git a/pyblp/configurations/optimization.py b/pyblp/configurations/optimization.py
index 1bc91ef..b9ce305 100644
--- a/pyblp/configurations/optimization.py
+++ b/pyblp/configurations/optimization.py
@@ -30,11 +30,11 @@ class Optimization(StringRepresentation):
         gradients:
 
             - ``'knitro'`` - Uses an installed version of
-              `Artleys Knitro <https://www.artelys.com/en/optimization-tools/knitro>`_. Python 3 is supported by Knitro
-              version 10.3 and newer. A number of environment variables most likely need to be configured properly, such
-              as ``KNITRODIR``, ``ARTELYS_LICENSE``, ``LD_LIBRARY_PATH`` (on Linux), and ``DYLD_LIBRARY_PATH`` (on Mac
-              OS X). For more information, refer to the
-              `Knitro installation guide <https://www.artelys.com/tools/knitro_doc/1_introduction/installation.html>`_.
+              `Artleys Knitro <https://www.artelys.com/solvers/knitro/>`_. Python 3 is supported by Knitro version 10.3
+              and newer. A number of environment variables most likely need to be configured properly, such as
+              ``KNITRODIR``, ``ARTELYS_LICENSE``, ``LD_LIBRARY_PATH`` (on Linux), and ``DYLD_LIBRARY_PATH`` (on
+              Mac OS X). For more information, refer to the
+              `Knitro installation guide <https://www.artelys.com/docs/knitro//1_introduction/installation.html>`_.
 
             - ``'slsqp'`` - Uses the :func:`scipy.optimize.minimize` SLSQP routine.
 
@@ -88,7 +88,7 @@ class Optimization(StringRepresentation):
         options are available for each optimization routine.
 
         If ``method`` is ``'knitro'``, these options should be
-        `Knitro user options <https://www.artelys.com/tools/knitro_doc/3_referenceManual/userOptions.html>`_. The
+        `Knitro user options <https://www.artelys.com/docs/knitro//3_referenceManual/userOptions.html>`_. The
         non-standard ``knitro_dir`` option can also be specified. The following options have non-standard default
         values: