diff --git a/docs/src/lib/interfaces.md b/docs/src/lib/interfaces.md
index 8707db84e79..762624da912 100644
--- a/docs/src/lib/interfaces.md
+++ b/docs/src/lib/interfaces.md
@@ -562,6 +562,14 @@ A ball is a centrally-symmetric set with a characteristic p-norm.
 AbstractBallp
 ```
 
+This interface defines the following functions:
+
+```@docs
+σ(::AbstractVector, ::AbstractBallp)
+ρ(::AbstractVector, ::AbstractBallp)
+∈(::AbstractVector, ::AbstractBallp)
+```
+
 ### Implementations
 
 * [Ball1](@ref def_Ball1)
diff --git a/docs/src/lib/sets/Ballp.md b/docs/src/lib/sets/Ballp.md
index ec2b097060a..4ea6d218eab 100644
--- a/docs/src/lib/sets/Ballp.md
+++ b/docs/src/lib/sets/Ballp.md
@@ -9,9 +9,6 @@ Ballp
 center(::Ballp)
 radius_ball(::Ballp)
 ball_norm(::Ballp)
-σ(::AbstractVector, ::Ballp)
-ρ(::AbstractVector, ::Ballp)
-∈(::AbstractVector, ::Ballp)
 rand(::Type{Ballp})
 translate(::Ballp, ::AbstractVector)
 translate!(::Ballp, ::AbstractVector)
@@ -33,3 +30,8 @@ Inherited from [`AbstractCentrallySymmetric`](@ref):
 * [`an_element`](@ref an_element(::AbstractCentrallySymmetric))
 * [`extrema`](@ref extrema(::AbstractCentrallySymmetric))
 * [`extrema`](@ref extrema(::AbstractCentrallySymmetric, ::Int))
+
+Inherited from [`AbstractBallp`](@ref):
+* [`σ`](@ref σ(::AbstractVector, ::AbstractBallp))
+* [`ρ`](@ref ρ(::AbstractVector, ::AbstractBallp))
+* [`∈`](@ref ∈(::AbstractVector, ::AbstractBallp))
diff --git a/src/Interfaces/AbstractBallp.jl b/src/Interfaces/AbstractBallp.jl
index 68ccaea804e..0b747976f03 100644
--- a/src/Interfaces/AbstractBallp.jl
+++ b/src/Interfaces/AbstractBallp.jl
@@ -61,3 +61,137 @@ end
 function _high_AbstractBallp(B::LazySet, i::Int)
     return center(B, i) + radius_ball(B)
 end
+
+
+"""
+    σ(d::AbstractVector, B::AbstractBallp)
+
+Return the support vector of a ball in the p-norm in a given direction.
+
+### Input
+
+- `d` -- direction
+- `B` -- ball in the p-norm
+
+### Output
+
+The support vector in the given direction.
+If the direction has norm zero, the center of the ball is returned.
+
+### Algorithm
+
+The support vector of the unit ball in the ``p``-norm along direction ``d`` is:
+
+```math
+σ(d, \\mathcal{B}_p^n(0, 1)) = \\dfrac{\\tilde{v}}{‖\\tilde{v}‖_q},
+```
+where ``\\tilde{v}_i = \\frac{|d_i|^q}{d_i}`` if ``d_i ≠ 0`` and
+``\\tilde{v}_i = 0`` otherwise, for all ``i=1,…,n``, and ``q`` is the conjugate
+number of ``p``.
+By the affine transformation ``x = r\\tilde{x} + c``, one obtains that
+the support vector of ``\\mathcal{B}_p^n(c, r)`` is
+
+```math
+σ(d, \\mathcal{B}_p^n(c, r)) = \\dfrac{v}{‖v‖_q},
+```
+where ``v_i = c_i + r\\frac{|d_i|^q}{d_i}`` if ``d_i ≠ 0`` and ``v_i = 0``
+otherwise, for all ``i = 1, …, n``.
+"""
+function σ(d::AbstractVector, B::AbstractBallp)
+    p = ball_norm(B)
+    q = p / (p - 1)
+    v = similar(d)
+    N = promote_type(eltype(d), eltype(B))
+    @inbounds for (i, di) in enumerate(d)
+        v[i] = di == zero(N) ? di : abs.(di) .^ q / di
+    end
+    vnorm = norm(v, p)
+    if isapproxzero(vnorm)
+        svec = copy(center(B))
+    else
+        svec = center(B) .+ radius_ball(B) .* (v ./ vnorm)
+    end
+    return svec
+end
+
+"""
+    ρ(d::AbstractVector, B::AbstractBallp)
+
+Evaluate the support function of a ball in the p-norm in the given direction.
+
+### Input
+
+- `d` -- direction
+- `B` -- ball in the p-norm
+
+### Output
+
+Evaluation of the support function in the given direction.
+
+### Algorithm
+
+Let ``c`` and ``r`` be the center and radius of the ball ``B`` in the p-norm,
+respectively, and let ``q = \\frac{p}{p-1}``. Then:
+
+```math
+ρ(d, B) = ⟨d, c⟩ + r ‖d‖_q.
+```
+"""
+function ρ(d::AbstractVector, B::AbstractBallp)
+    p = ball_norm(B)
+    q = p / (p - 1)
+    return dot(d, center(B)) + radius_ball(B) * norm(d, q)
+end
+
+"""
+    ∈(x::AbstractVector, B::AbstractBallp)
+
+Check whether a given point is contained in a ball in the p-norm.
+
+### Input
+
+- `x` -- point/vector
+- `B` -- ball in the p-norm
+
+### Output
+
+`true` iff ``x ∈ B``.
+
+### Notes
+
+This implementation is worst-case optimized, i.e., it is optimistic and first
+computes (see below) the whole sum before comparing to the radius.
+In applications where the point is typically far away from the ball, a fail-fast
+implementation with interleaved comparisons could be more efficient.
+
+### Algorithm
+
+Let ``B`` be an ``n``-dimensional ball in the p-norm with radius ``r`` and let
+``c_i`` and ``x_i`` be the ball's center and the vector ``x`` in dimension
+``i``, respectively.
+Then ``x ∈ B`` iff ``\\left( ∑_{i=1}^n |c_i - x_i|^p \\right)^{1/p} ≤ r``.
+
+### Examples
+
+```jldoctest
+julia> B = Ballp(1.5, [1.0, 1.0], 1.)
+Ballp{Float64, Vector{Float64}}(1.5, [1.0, 1.0], 1.0)
+
+julia> [0.5, -0.5] ∈ B
+false
+
+julia> [0.5, 1.5] ∈ B
+true
+```
+"""
+function ∈(x::AbstractVector, B::AbstractBallp)
+    @assert length(x) == dim(B) "a vector of length $(length(x)) is " *
+                                "incompatible with a set of dimension $(dim(B))"
+    N = promote_type(eltype(x), eltype(B))
+    p = ball_norm(B)
+    sum = zero(N)
+    @inbounds for i in eachindex(x)
+        sum += abs(center(B, i) - x[i])^p
+    end
+    return _leq(sum^(one(N) / p), radius_ball(B))
+end
diff --git a/src/Sets/Ballp.jl b/src/Sets/Ballp.jl
index b3fd40eca1c..1ad88a3a767 100644
--- a/src/Sets/Ballp.jl
+++ b/src/Sets/Ballp.jl
@@ -128,137 +128,6 @@ function ball_norm(B::Ballp)
     return B.p
 end
 
-"""
-    σ(d::AbstractVector, B::Ballp)
-
-Return the support vector of a ball in the p-norm in a given direction.
-
-### Input
-
-- `d` -- direction
-- `B` -- ball in the p-norm
-
-### Output
-
-The support vector in the given direction.
-If the direction has norm zero, the center of the ball is returned.
-
-### Algorithm
-
-The support vector of the unit ball in the ``p``-norm along direction ``d`` is:
-
-```math
-σ(d, \\mathcal{B}_p^n(0, 1)) = \\dfrac{\\tilde{v}}{‖\\tilde{v}‖_q},
-```
-where ``\\tilde{v}_i = \\frac{|d_i|^q}{d_i}`` if ``d_i ≠ 0`` and
-``\\tilde{v}_i = 0`` otherwise, for all ``i=1,…,n``, and ``q`` is the conjugate
-number of ``p``.
-By the affine transformation ``x = r\\tilde{x} + c``, one obtains that
-the support vector of ``\\mathcal{B}_p^n(c, r)`` is
-
-```math
-σ(d, \\mathcal{B}_p^n(c, r)) = \\dfrac{v}{‖v‖_q},
-```
-where ``v_i = c_i + r\\frac{|d_i|^q}{d_i}`` if ``d_i ≠ 0`` and ``v_i = 0``
-otherwise, for all ``i = 1, …, n``.
-"""
-function σ(d::AbstractVector, B::Ballp)
-    p = B.p
-    q = p / (p - 1)
-    v = similar(d)
-    N = promote_type(eltype(d), eltype(B))
-    @inbounds for (i, di) in enumerate(d)
-        v[i] = di == zero(N) ? di : abs.(di) .^ q / di
-    end
-    vnorm = norm(v, p)
-    if isapproxzero(vnorm)
-        svec = B.center
-    else
-        svec = @.(B.center + B.radius * (v / vnorm))
-    end
-    return svec
-end
-
-"""
-    ρ(d::AbstractVector, B::Ballp)
-
-Evaluate the support function of a ball in the p-norm in the given direction.
-
-### Input
-
-- `d` -- direction
-- `B` -- ball in the p-norm
-
-### Output
-
-Evaluation of the support function in the given direction.
-
-### Algorithm
-
-Let ``c`` and ``r`` be the center and radius of the ball ``B`` in the p-norm,
-respectively, and let ``q = \\frac{p}{p-1}``. Then:
-
-```math
-ρ(d, B) = ⟨d, c⟩ + r ‖d‖_q.
-```
-"""
-function ρ(d::AbstractVector, B::Ballp)
-    q = B.p / (B.p - 1)
-    return dot(d, B.center) + B.radius * norm(d, q)
-end
-
-"""
-    ∈(x::AbstractVector, B::Ballp)
-
-Check whether a given point is contained in a ball in the p-norm.
-
-### Input
-
-- `x` -- point/vector
-- `B` -- ball in the p-norm
-
-### Output
-
-`true` iff ``x ∈ B``.
-
-### Notes
-
-This implementation is worst-case optimized, i.e., it is optimistic and first
-computes (see below) the whole sum before comparing to the radius.
-In applications where the point is typically far away from the ball, a fail-fast
-implementation with interleaved comparisons could be more efficient.
-
-### Algorithm
-
-Let ``B`` be an ``n``-dimensional ball in the p-norm with radius ``r`` and let
-``c_i`` and ``x_i`` be the ball's center and the vector ``x`` in dimension
-``i``, respectively.
-Then ``x ∈ B`` iff ``\\left( ∑_{i=1}^n |c_i - x_i|^p \\right)^{1/p} ≤ r``.
-
-### Examples
-
-```jldoctest
-julia> B = Ballp(1.5, [1.0, 1.0], 1.)
-Ballp{Float64, Vector{Float64}}(1.5, [1.0, 1.0], 1.0)
-
-julia> [0.5, -0.5] ∈ B
-false
-
-julia> [0.5, 1.5] ∈ B
-true
-```
-"""
-function ∈(x::AbstractVector, B::Ballp)
-    @assert length(x) == dim(B) "a vector of length $(length(x)) is " *
-                                "incompatible with a set of dimension $(dim(B))"
-    N = promote_type(eltype(x), eltype(B))
-    sum = zero(N)
-    for i in eachindex(x)
-        sum += abs(B.center[i] - x[i])^B.p
-    end
-    return _leq(sum^(one(N) / B.p), B.radius)
-end
-
 """
     rand(::Type{Ballp}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
          [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing)