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