From d59c41c874612cf8d2ae93518a441768da952e54 Mon Sep 17 00:00:00 2001
From: shooter74 <48887113+shooter74@users.noreply.github.com>
Date: Sun, 24 Mar 2019 19:18:55 +0100
Subject: [PATCH] Update README.md

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 # AutomaticDifferentiation
 Templated c++ Forward Automatic differentiation.
+
+There are two versions :
+- A scalar one,
+- a vectorized one.
+
+The class is a simple one, no expression templates are used. The class is however a template, meaning that any base numeric type can be used with it. It has successfully tested with boost::multiprecision::mpfr.
+
+## Scalar version
+The scalar one allows very easily to produce higher order derivatives.
+
+## Vector version
+The vectorized one is harder to make work with higher order derivatives, but allows the simultaneous computation of the full gradient, in a single function call, making it more efficient than backward automatic differentiation.
+It currently depends on Eigen for the vectorized part.