Rename value_and_derivatives -> value_and_derivative_and_second_deriv…

…ative
JuliaDiff · Jan 3, 2024 · 3b7a5b5 · 3b7a5b5
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diff --git a/docs/src/user_guide.md b/docs/src/user_guide.md
@@ -73,7 +73,7 @@ AbstractDifferentiation.value_and_gradient
 AbstractDifferentiation.value_and_jacobian
 AbstractDifferentiation.value_and_second_derivative
 AbstractDifferentiation.value_and_hessian
-AbstractDifferentiation.value_and_derivatives
+AbstractDifferentiation.value_derivative_and_second_derivative
 AbstractDifferentiation.value_gradient_and_hessian
 ```
 

diff --git a/ext/AbstractDifferentiationForwardDiffExt.jl b/ext/AbstractDifferentiationForwardDiffExt.jl
@@ -90,7 +90,7 @@ function AD.value_and_hessian(ba::AD.ForwardDiffBackend, f, x)
     return DiffResults.value(result), (DiffResults.hessian(result),)
 end
 
-function AD.value_and_derivatives(ba::AD.ForwardDiffBackend, f, x::Real)
+function AD.value_derivative_and_second_derivative(ba::AD.ForwardDiffBackend, f, x::Real)
     T = typeof(ForwardDiff.Tag(f, typeof(x)))
     xdual = ForwardDiff.Dual{T}(x, one(x))
     T2 = typeof(ForwardDiff.Tag(f, typeof(xdual)))

diff --git a/src/AbstractDifferentiation.jl b/src/AbstractDifferentiation.jl
@@ -206,11 +206,11 @@ function value_and_hessian(ab::HigherOrderBackend, f, x)
 end
 
 """
-    AD.value_and_derivatives(ab::AD.AbstractBackend, f, x)
+    AD.value_derivative_and_second_derivative(ab::AD.AbstractBackend, f, x)
 
 Return the tuple `(v, d, d2)` of the function value `v = f(x)` and the first and second derivatives `d = AD.derivative(ab, f, x)` and `d2 = AD.secondderivative(ab, f, x)`.
 """
-function value_and_derivatives(ab::AbstractBackend, f, x)
+function value_derivative_and_second_derivative(ab::AbstractBackend, f, x)
     if x isa Tuple
         # only support computation of Hessian for functions with single input argument
         x = only(x)

diff --git a/test/test_utils.jl b/test/test_utils.jl
@@ -174,7 +174,7 @@ function test_second_derivatives(backend; multiple_inputs=false, test_types=true
     end
     @test valscalar == fder(xscalar, yscalar)
     @test norm.(dder2 .- dder1) == (0,)
-    valscalar, der, dder3 = AD.value_and_derivatives(
+    valscalar, der, dder3 = AD.value_derivative_and_second_derivative(
         backend, x -> fder(x, yscalar), xscalar
     )
     if test_types