Copyedit documentation: typos, spelling
diff --git a/doc/TutorialReductionsVisitorsBroadcasting.dox b/doc/TutorialReductionsVisitorsBroadcasting.dox
index 6d25ff0..f5322b4 100644
--- a/doc/TutorialReductionsVisitorsBroadcasting.dox
+++ b/doc/TutorialReductionsVisitorsBroadcasting.dox
@@ -101,17 +101,16 @@
\verbinclude Tutorial_ReductionsVisitorsBroadcasting_visitors.out
</td></tr></table>
-Note that both functions also return the value of the minimum or maximum coefficient if needed,
-as if it was a typical reduction operation.
+Both functions also return the value of the minimum or maximum coefficient.
\section TutorialReductionsVisitorsBroadcastingPartialReductions Partial reductions
Partial reductions are reductions that can operate column- or row-wise on a Matrix or
Array, applying the reduction operation on each column or row and
-returning a column or row-vector with the corresponding values. Partial reductions are applied
+returning a column or row vector with the corresponding values. Partial reductions are applied
with \link DenseBase::colwise() colwise() \endlink or \link DenseBase::rowwise() rowwise() \endlink.
A simple example is obtaining the maximum of the elements
-in each column in a given matrix, storing the result in a row-vector:
+in each column in a given matrix, storing the result in a row vector:
<table class="example">
<tr><th>Example:</th><th>Output:</th></tr>
@@ -133,8 +132,7 @@
\verbinclude Tutorial_ReductionsVisitorsBroadcasting_rowwise.out
</td></tr></table>
-<b>Note that column-wise operations return a 'row-vector' while row-wise operations
-return a 'column-vector'</b>
+<b>Note that column-wise operations return a row vector, while row-wise operations return a column vector.</b>
\subsection TutorialReductionsVisitorsBroadcastingPartialReductionsCombined Combining partial reductions with other operations
It is also possible to use the result of a partial reduction to do further processing.
@@ -176,7 +174,7 @@
constructs an expression where a vector (column or row) is interpreted as a matrix by replicating it in
one direction.
-A simple example is to add a certain column-vector to each column in a matrix.
+A simple example is to add a certain column vector to each column in a matrix.
This can be accomplished with:
<table class="example">
@@ -253,7 +251,7 @@
\f]
- <tt>(m.colwise() - v).colwise().squaredNorm()</tt> is a partial reduction, computing the squared norm column-wise. The result of
-this operation is a row-vector where each coefficient is the squared Euclidean distance between each column in <tt>m</tt> and <tt>v</tt>: \f[
+this operation is a row vector where each coefficient is the squared Euclidean distance between each column in <tt>m</tt> and <tt>v</tt>: \f[
\mbox{(m.colwise() - v).colwise().squaredNorm()} =
\begin{bmatrix}
1 & 505 & 32 & 50
diff --git a/doc/UsingIntelMKL.dox b/doc/UsingIntelMKL.dox
index 84db992..02c62ad 100644
--- a/doc/UsingIntelMKL.dox
+++ b/doc/UsingIntelMKL.dox
@@ -52,7 +52,7 @@
These substitutions apply only for \b Dynamic \b or \b large enough objects with one of the following four standard scalar types: \c float, \c double, \c complex<float>, and \c complex<double>.
Operations on other scalar types or mixing reals and complexes will continue to use the built-in algorithms.
-In addition you can coarsely select choose which parts will be substituted by defining one or multiple of the following macros:
+In addition you can choose which parts will be substituted by defining one or multiple of the following macros:
<table class="manual">
<tr><td>\c EIGEN_USE_BLAS </td><td>Enables the use of external BLAS level 2 and 3 routines (currently works with Intel MKL only)</td></tr>
