Update README.md

README.md

@@ -8,43 +8,62 @@
 
 ForwardDiff implements methods to take **derivatives**, **gradients**, **Jacobians**, **Hessians**, and higher-order derivatives of native Julia functions (or any callable object, really) using **forward mode automatic differentiation (AD)**.
 
-While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff **generally outperform non-AD algorithms in both speed and accuracy.**
+While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy.
 
 Here's a simple example showing the package in action:
 
 ```julia
 julia> using ForwardDiff
 
-julia> f(x::Vector) = sum(sin, x) + prod(tan, x) * sum(sqrt, x);
+julia> f(x::Vector) = sin(x[1]) + prod(x[2:end]);  # returns a scalar
 
-julia> x = rand(5) # small size for example's sake
-5-element Array{Float64,1}:
- 0.986403
- 0.140913
- 0.294963
- 0.837125
- 0.650451
+julia> x = vcat(pi/4, 2:4)
+4-element Vector{Float64}:
+ 0.7853981633974483
+ 2.0
+ 3.0
+ 4.0
 
-julia> g = x -> ForwardDiff.gradient(f, x); # g = ∇f
-
-julia> g(x)
-5-element Array{Float64,1}:
- 1.01358
- 2.50014
- 1.72574
- 1.10139
- 1.2445
+julia> ForwardDiff.gradient(f, x)
+4-element Vector{Float64}:
+  0.7071067811865476
+ 12.0
+  8.0
+  6.0
 
 julia> ForwardDiff.hessian(f, x)
-5x5 Array{Float64,2}:
- 0.585111  3.48083  1.7706    0.994057  1.03257
- 3.48083   1.06079  5.79299   3.25245   3.37871
- 1.7706    5.79299  0.423981  1.65416   1.71818
- 0.994057  3.25245  1.65416   0.251396  0.964566
- 1.03257   3.37871  1.71818   0.964566  0.140689
- ```
-
- Trying to switch to the latest version of ForwardDiff? See our [upgrade guide](http://www.juliadiff.org/ForwardDiff.jl/stable/user/upgrade/) for details regarding user-facing changes between releases.
+4×4 Matrix{Float64}:
+ -0.707107  0.0  0.0  0.0
+  0.0       0.0  4.0  3.0
+  0.0       4.0  0.0  2.0
+  0.0       3.0  2.0  0.0
+```
+
+Functions like `f` which map a vector to a scalar are the best case for reverse-mode automatic differentiation,
+but ForwardDiff may still be a good choice if `x` is not too large, as it is much simpler.
+The best case for forward-mode differentiation is a function which maps a scalar to a vector, like this `g`:
+
+```julia
+julia> g(y::Real) = [sin(y), cos(y), tan(y)];  # returns a vector
+
+julia> ForwardDiff.derivative(g, pi/4)
+3-element Vector{Float64}:
+  0.7071067811865476
+ -0.7071067811865475
+  1.9999999999999998
+
+julia> ForwardDiff.jacobian(x) do x  # anonymous function, returns a length-2 vector
+         [sin(x[1]), prod(x[2:end])]
+       end
+2×4 Matrix{Float64}:
+ 0.707107   0.0  0.0  0.0
+ 0.0       12.0  8.0  6.0
+```
+
+See [ForwardDiff's documentation](https://juliadiff.org/ForwardDiff.jl/stable) for full details on how to use this package.
+ForwardDiff relies on [DiffRules](https://github.com/JuliaDiff/DiffRules.jl) for the derivatives of many simple function such as `sin`.
+
+See the [JuliaDiff web page](https://juliadiff.org) for other automatic differentiation packages.
 
 ## Publications