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Fix #499 missing linearization method #500

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Merged
bvdmitri merged 1 commit into from
Sep 9, 2025
Merged

Fix #499 missing linearization method #500

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7 changes: 7 additions & 0 deletions src/approximations/linearization.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -77,6 +77,13 @@ function local_linearization(r::Real, splitg::S, x_hat::H) where {S, H}
return local_linearization_split(r, fA, x_hat)
end

# In case if `g(x_hat)` returns a vector, but input is a number
function local_linearization(result::AbstractVector, g::G, x_hat::Tuple{T}) where {G, T <: Real}
A = ForwardDiff.derivative(g, first(x_hat))
b = result - A * first(x_hat)
return (A, b)
end

# In case if `g(x_hat)` returns a vector, but inputs are numbers
function local_linearization(r::AbstractVector, splitg::S, x_hat::H) where {S, H}
# `r` is a vector, so we need to use `jacobian` instead of `gradient`
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1 change: 1 addition & 0 deletions test/approximations/linearization_tests.jl
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Expand Up @@ -11,5 +11,6 @@
@test @inferred(approximate(Linearization(), (x, y) -> x - y, (1, 2))) == ([1 -1], 0)
@test @inferred(approximate(Linearization(), (x, y) -> x .- y, ([1.0, 2.0], 1.0))) == ([1.0 0.0 -1.0; 0.0 1.0 -1.0], [0.0, 0.0])
@test @inferred(approximate(Linearization(), (x, y) -> x .- y, ([1.0, 2.0], [1.0, 1.0]))) == ([1.0 0.0 -1.0 0.0; 0.0 1.0 0.0 -1.0], [0.0, 0.0])
@test @inferred(approximate(Linearization(), (x) -> x .- [1, 1], (1.0,))) == ([1.0, 1.0], [-1.0, -1.0])
end
end