Add script for building documentation.
Update make_release
Minor documentation fixes.
Change-Id: I1248ec3f58be66b5929aee6f2aa392c15d53ed83
diff --git a/docs/source/bibliography.rst b/docs/source/bibliography.rst
index e80e483..188ed77 100644
--- a/docs/source/bibliography.rst
+++ b/docs/source/bibliography.rst
@@ -19,7 +19,7 @@
**Representations of Quasi-Newton Matrices and their use in Limited
Memory Methods**, *Mathematical Programming* 63(4):129–-156, 1994.
-.. [ByrdSchanbel] R.H. Byrd, R.B. Schnabel, and G.A. Shultz, **Approximate
+.. [ByrdSchnabel] R.H. Byrd, R.B. Schnabel, and G.A. Shultz, **Approximate
solution of the trust region problem by minimization over
two dimensional subspaces**, *Mathematical programming*,
40(1):247–263, 1988.
diff --git a/docs/source/solving.rst b/docs/source/solving.rst
index fb48bb3..7f58ec8 100644
--- a/docs/source/solving.rst
+++ b/docs/source/solving.rst
@@ -231,8 +231,8 @@
``SUBSPACE_DOGLEG`` is a more sophisticated method that considers the
entire two dimensional subspace spanned by these two vectors and finds
-the point that minimizes the trust region problem in this
-subspace [ByrdSchanbel]_.
+the point that minimizes the trust region problem in this subspace
+[ByrdSchnabel]_.
The key advantage of the Dogleg over Levenberg Marquardt is that if
the step computation for a particular choice of :math:`\mu` does not
@@ -792,7 +792,7 @@
``ARMIJO`` is the only choice right now.
-.. member:: NonlinearConjugateGradientType Solver::Options::nonlinear conjugate_gradient_type
+.. member:: NonlinearConjugateGradientType Solver::Options::nonlinear_conjugate_gradient_type
Default: ``FLETCHER_REEVES``
@@ -837,8 +837,8 @@
Ceres supports two different dogleg strategies.
``TRADITIONAL_DOGLEG`` method by Powell and the ``SUBSPACE_DOGLEG``
- method described by [ByrdSchnabel]_. See :ref:`section-dogleg` for more
- details.
+ method described by [ByrdSchnabel]_ . See :ref:`section-dogleg`
+ for more details.
.. member:: bool Solver::Options::use_nonmonotonic_steps
