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Remove Optim.jl interface + minor tidying up of src/optimisation/Optimisation #2708

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Remove Optim.jl interface + minor tidying up of src/optimisation/Optimisation #2708

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6bceedb
Remove Optim.jl extension
penelopeysm Nov 7, 2025
676f11e
Simplify OptimLogDensity construction
penelopeysm Nov 3, 2025
3bb2b42
Changelog
penelopeysm Nov 3, 2025
6448372
Remove Optim.jl interface tests
penelopeysm Nov 3, 2025
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9 changes: 9 additions & 0 deletions HISTORY.md
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Expand Up @@ -18,6 +18,15 @@ As long as the above functions are defined correctly, Turing will be able to use

The `Turing.Inference.isgibbscomponent(::MySampler)` interface function still exists, but in this version the default has been changed to `true`, so you should not need to overload this.

## Optimisation interface

The Optim.jl interface has been removed (so you cannot call `Optim.optimize` directly on Turing models).
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@yebai yebai Nov 3, 2025

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I suggest that we define our own optimize function on top of DynamicPPL.LogDensityFunction to avoid the breaking change. The optimize function provides a natural alternative to AbstractMCMC.sample.

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@penelopeysm penelopeysm Nov 3, 2025

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I don't really agree:

  1. What is the purpose of avoiding the breaking change? The functionality is already present in maximum_likelihood(model) or maximum_a_posteriori(model) so this is just duplicate functionality.

  2. A Turing.optimize function would still be a breaking change because this is not the same as Optim.optimize.

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@yebai yebai Nov 4, 2025 •
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Thanks, Penny, for clarifying.

My main point is not breaking change, instead optimise mirrors the Turing.sample API well, and provides a generic interface for optimisation, and VI algorithms. Sorry for the confusion. Though I agree we ought to remove the Optim interface and define our own optimize method.

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@penelopeysm penelopeysm Nov 4, 2025

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Sure, in that case we could start by renaming estimate_mode to optimize, and exporting it. That would give us out of the box

optimize(model, MLE(); kwargs...)
optimize(model, MAP(); kwargs...)

and then the VI arguments could be bundled into a struct that would be passed as the second argument.

You can use the `maximum_likelihood` or `maximum_a_posteriori` functions with an Optim.jl solver instead (via Optimization.jl: see https://docs.sciml.ai/Optimization/stable/optimization_packages/optim/ for documentation of the available solvers).

## Internal changes

The constructors of `OptimLogDensity` have been replaced with a single constructor, `OptimLogDensity(::DynamicPPL.LogDensityFunction)`.

# 0.41.1

The `ModeResult` struct returned by `maximum_a_posteriori` and `maximum_likelihood` can now be wrapped in `InitFromParams()`.
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7 changes: 0 additions & 7 deletions Project.toml
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Expand Up @@ -41,11 +41,9 @@ StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"

[weakdeps]
DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"

[extensions]
TuringDynamicHMCExt = "DynamicHMC"
TuringOptimExt = ["Optim", "AbstractPPL"]

[compat]
ADTypes = "1.9"
Expand All @@ -72,7 +70,6 @@ LinearAlgebra = "1"
LogDensityProblems = "2"
MCMCChains = "5, 6, 7"
NamedArrays = "0.9, 0.10"
Optim = "1"
Optimization = "3, 4, 5"
OptimizationOptimJL = "0.1, 0.2, 0.3, 0.4"
OrderedCollections = "1"
Expand All @@ -86,7 +83,3 @@ StatsAPI = "1.6"
StatsBase = "0.32, 0.33, 0.34"
StatsFuns = "0.8, 0.9, 1"
julia = "1.10.8"

[extras]
DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"
198 changes: 0 additions & 198 deletions ext/TuringOptimExt.jl
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