Kashif's corrections to the docs
diff --git a/docs/api.tex b/docs/api.tex
index 2c36377..a9f4aef 100644
--- a/docs/api.tex
+++ b/docs/api.tex
@@ -126,7 +126,7 @@
Dimension of y -------------------+
\end{minted}
-In this example, there is usually an instance for each measumerent of k.
+In this example, there is usually an instance for each measurement of k.
In the instantiation above, the template parameters following
\texttt{MyScalarCostFunction}, \texttt{<1, 2, 2>} describe the functor as computing a
@@ -150,7 +150,7 @@
To get a numerically differentiated cost function, define a subclass of
\texttt{CostFunction} such that the \texttt{Evaluate} function ignores the jacobian
parameter. The numeric differentiation wrapper will fill in the jacobians array
- if nececssary by repeatedly calling the \texttt{Evaluate} method with
+ if necessary by repeatedly calling the \texttt{Evaluate} method with
small changes to the appropriate parameters, and computing the slope. For
performance, the numeric differentiation wrapper class is templated on the
concrete cost function, even though it could be implemented only in terms of
@@ -582,7 +582,7 @@
The finite differencing is done along each dimension. The
reason to use a relative (rather than absolute) step size is
- that this way, numeric differentation works for functions where
+ that this way, numeric differentiation works for functions where
the arguments are typically large (e.g. 1e9) and when the
values are small (e.g. 1e-5). It is possible to construct
"torture cases" which break this finite difference heuristic,
