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Fixup
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@lostella
lostella authored Jan 6, 2024
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2 changes: 1 addition & 1 deletion docs/src/guide/custom_objectives.jl
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#
# Defining the proximal mapping for a custom function type requires adding a method for [`ProximalCore.prox!`](@ref).
#
# To compute gradients, algorithms use [`ProximalAlgorithm.value_and_pullback`](@ref):
# To compute gradients, algorithms use [`ProximalAlgorithms.value_and_pullback`](@ref):
# this relies on [AbstractDifferentiation](https://github.com/JuliaDiff/AbstractDifferentiation.jl), for automatic differentiation
# with any of its supported backends, when functions are wrapped in [`ProximalAlgorithms.AutoDifferentiable`](@ref),
# as the esamples below show.
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