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SciML · Dec 26, 2023 · 6687491 · 6687491
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diff --git a/docs/src/API/optimization_function.md b/docs/src/API/optimization_function.md
@@ -15,6 +15,7 @@ The choices for the auto-AD fill-ins with quick descriptions are:
   - `AutoFiniteDiff()`: Finite differencing, not optimal but always applicable
   - `AutoModelingToolkit()`: The fastest choice for large scalar optimizations
   - `AutoEnzyme()`: Highly performant AD choice for type stable and optimized code
+
 ## Automatic Differentiation Choice API
 
 The following sections describe the Auto-AD choices in detail.

diff --git a/docs/src/optimization_packages/polyopt.md b/docs/src/optimization_packages/polyopt.md
@@ -1,6 +1,6 @@
 # OptimizationPolyalgorithms.jl
 
- OptimizationPolyalgorithms.jl is a package for collecting polyalgorithms formed by fusing popular optimization solvers of different characteristics.
+OptimizationPolyalgorithms.jl is a package for collecting polyalgorithms formed by fusing popular optimization solvers of different characteristics.
 
 ## Installation: OptimizationPolyalgorithms
 
@@ -10,6 +10,7 @@ To use this package, install the OptimizationPolyalgorithms package:
 import Pkg;
 Pkg.add("OptimizationPolyalgorithms");
 ```
+
 ## Algorithms
 
 Right now we support the following polyalgorithms.

diff --git a/ext/OptimizationEnzymeExt.jl b/ext/OptimizationEnzymeExt.jl
@@ -16,8 +16,8 @@ isdefined(Base, :get_extension) ? (using Enzyme) : (using ..Enzyme)
 end
 
 function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
-    adtype::AutoEnzyme, p,
-    num_cons = 0)
+        adtype::AutoEnzyme, p,
+        num_cons = 0)
     if f.grad === nothing
         grad = let
             function (res, θ, args...)
@@ -154,9 +154,9 @@ function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
 end
 
 function Optimization.instantiate_function(f::OptimizationFunction{true},
-    cache::Optimization.ReInitCache,
-    adtype::AutoEnzyme,
-    num_cons = 0)
+        cache::Optimization.ReInitCache,
+        adtype::AutoEnzyme,
+        num_cons = 0)
     p = cache.p
 
     if f.grad === nothing

diff --git a/ext/OptimizationFiniteDiffExt.jl b/ext/OptimizationFiniteDiffExt.jl
@@ -9,7 +9,7 @@ isdefined(Base, :get_extension) ? (using FiniteDiff) : (using ..FiniteDiff)
 const FD = FiniteDiff
 
 function Optimization.instantiate_function(f, x, adtype::AutoFiniteDiff, p,
-    num_cons = 0)
+        num_cons = 0)
     _f = (θ, args...) -> first(f.f(θ, p, args...))
     updatecache = (cache, x) -> (cache.xmm .= x; cache.xmp .= x; cache.xpm .= x; cache.xpp .= x; return cache)
 
@@ -117,7 +117,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoFiniteDiff, p,
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoFiniteDiff, num_cons = 0)
+        adtype::AutoFiniteDiff, num_cons = 0)
     _f = (θ, args...) -> first(f.f(θ, cache.p, args...))
     updatecache = (cache, x) -> (cache.xmm .= x; cache.xmp .= x; cache.xpm .= x; cache.xpp .= x; return cache)
 

diff --git a/ext/OptimizationForwardDiffExt.jl b/ext/OptimizationForwardDiffExt.jl
@@ -14,8 +14,8 @@ function default_chunk_size(len)
 end
 
 function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
-    adtype::AutoForwardDiff{_chunksize}, p,
-    num_cons = 0) where {_chunksize}
+        adtype::AutoForwardDiff{_chunksize}, p,
+        num_cons = 0) where {_chunksize}
     chunksize = _chunksize === nothing ? default_chunk_size(length(x)) : _chunksize
 
     _f = (θ, args...) -> first(f.f(θ, p, args...))
@@ -90,9 +90,9 @@ function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
 end
 
 function Optimization.instantiate_function(f::OptimizationFunction{true},
-    cache::Optimization.ReInitCache,
-    adtype::AutoForwardDiff{_chunksize},
-    num_cons = 0) where {_chunksize}
+        cache::Optimization.ReInitCache,
+        adtype::AutoForwardDiff{_chunksize},
+        num_cons = 0) where {_chunksize}
     chunksize = _chunksize === nothing ? default_chunk_size(length(cache.u0)) : _chunksize
 
     _f = (θ, args...) -> first(f.f(θ, cache.p, args...))

diff --git a/ext/OptimizationMTKExt.jl b/ext/OptimizationMTKExt.jl
@@ -7,7 +7,7 @@ import Optimization.ADTypes: AutoModelingToolkit
 isdefined(Base, :get_extension) ? (using ModelingToolkit) : (using ..ModelingToolkit)
 
 function Optimization.instantiate_function(f, x, adtype::AutoModelingToolkit, p,
-    num_cons = 0)
+        num_cons = 0)
     p = isnothing(p) ? SciMLBase.NullParameters() : p
 
     sys = ModelingToolkit.modelingtoolkitize(OptimizationProblem(f, x, p;
@@ -52,7 +52,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoModelingToolkit, p,
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoModelingToolkit, num_cons = 0)
+        adtype::AutoModelingToolkit, num_cons = 0)
     p = isnothing(cache.p) ? SciMLBase.NullParameters() : cache.p
 
     sys = ModelingToolkit.modelingtoolkitize(OptimizationProblem(f, cache.u0, cache.p;

diff --git a/ext/OptimizationReverseDiffExt.jl b/ext/OptimizationReverseDiffExt.jl
@@ -18,8 +18,8 @@ function default_chunk_size(len)
 end
 
 function Optimization.instantiate_function(f, x, adtype::AutoReverseDiff,
-    p = SciMLBase.NullParameters(),
-    num_cons = 0)
+        p = SciMLBase.NullParameters(),
+        num_cons = 0)
     _f = (θ, args...) -> first(f.f(θ, p, args...))
 
     chunksize = default_chunk_size(length(x))
@@ -151,7 +151,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoReverseDiff,
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoReverseDiff, num_cons = 0)
+        adtype::AutoReverseDiff, num_cons = 0)
     _f = (θ, args...) -> first(f.f(θ, cache.p, args...))
 
     chunksize = default_chunk_size(length(cache.u0))

diff --git a/ext/OptimizationSparseDiffExt.jl b/ext/OptimizationSparseDiffExt.jl
@@ -20,8 +20,8 @@ function default_chunk_size(len)
 end
 
 function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
-    adtype::AutoSparseForwardDiff{_chunksize}, p,
-    num_cons = 0) where {_chunksize}
+        adtype::AutoSparseForwardDiff{_chunksize}, p,
+        num_cons = 0) where {_chunksize}
     if maximum(getfield.(methods(f.f), :nargs)) > 3
         error("$(string(adtype)) with SparseDiffTools does not support functions with more than 2 arguments")
     end
@@ -127,9 +127,9 @@ function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
 end
 
 function Optimization.instantiate_function(f::OptimizationFunction{true},
-    cache::Optimization.ReInitCache,
-    adtype::AutoSparseForwardDiff{_chunksize},
-    num_cons = 0) where {_chunksize}
+        cache::Optimization.ReInitCache,
+        adtype::AutoSparseForwardDiff{_chunksize},
+        num_cons = 0) where {_chunksize}
     if maximum(getfield.(methods(f.f), :nargs)) > 3
         error("$(string(adtype)) with SparseDiffTools does not support functions with more than 2 arguments")
     end
@@ -237,7 +237,7 @@ end
 const FD = FiniteDiff
 
 function Optimization.instantiate_function(f, x, adtype::AutoSparseFiniteDiff, p,
-    num_cons = 0)
+        num_cons = 0)
     if maximum(getfield.(methods(f.f), :nargs)) > 3
         error("$(string(adtype)) with SparseDiffTools does not support functions with more than 2 arguments")
     end
@@ -362,7 +362,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoSparseFiniteDiff, p
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoSparseFiniteDiff, num_cons = 0)
+        adtype::AutoSparseFiniteDiff, num_cons = 0)
     if maximum(getfield.(methods(f.f), :nargs)) > 3
         error("$(string(adtype)) with SparseDiffTools does not support functions with more than 2 arguments")
     end
@@ -493,8 +493,8 @@ end
 struct OptimizationSparseReverseTag end
 
 function Optimization.instantiate_function(f, x, adtype::AutoSparseReverseDiff,
-    p = SciMLBase.NullParameters(),
-    num_cons = 0)
+        p = SciMLBase.NullParameters(),
+        num_cons = 0)
     _f = (θ, args...) -> first(f.f(θ, p, args...))
 
     chunksize = default_chunk_size(length(x))
@@ -668,7 +668,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoSparseReverseDiff,
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoSparseReverseDiff, num_cons = 0)
+        adtype::AutoSparseReverseDiff, num_cons = 0)
     _f = (θ, args...) -> first(f.f(θ, cache.p, args...))
 
     chunksize = default_chunk_size(length(cache.u0))

diff --git a/ext/OptimizationTrackerExt.jl b/ext/OptimizationTrackerExt.jl
@@ -6,7 +6,7 @@ import Optimization.ADTypes: AutoTracker
 isdefined(Base, :get_extension) ? (using Tracker) : (using ..Tracker)
 
 function Optimization.instantiate_function(f, x, adtype::AutoTracker, p,
-    num_cons = 0)
+        num_cons = 0)
     num_cons != 0 && error("AutoTracker does not currently support constraints")
     _f = (θ, args...) -> first(f.f(θ, p, args...))
 
@@ -37,7 +37,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoTracker, p,
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoTracker, num_cons = 0)
+        adtype::AutoTracker, num_cons = 0)
     num_cons != 0 && error("AutoTracker does not currently support constraints")
     _f = (θ, args...) -> first(f.f(θ, cache.p, args...))
 

diff --git a/ext/OptimizationZygoteExt.jl b/ext/OptimizationZygoteExt.jl
@@ -7,7 +7,7 @@ isdefined(Base, :get_extension) ? (using Zygote, Zygote.ForwardDiff) :
 (using ..Zygote, ..Zygote.ForwardDiff)
 
 function Optimization.instantiate_function(f, x, adtype::AutoZygote, p,
-    num_cons = 0)
+        num_cons = 0)
     _f = (θ, args...) -> f(θ, p, args...)[1]
     if f.grad === nothing
         grad = function (res, θ, args...)
@@ -84,7 +84,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoZygote, p,
 end
 
 function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
-    adtype::AutoZygote, num_cons = 0)
+        adtype::AutoZygote, num_cons = 0)
     _f = (θ, args...) -> f(θ, cache.p, args...)[1]
     if f.grad === nothing
         grad = function (res, θ, args...)

diff --git a/lib/OptimizationBBO/src/OptimizationBBO.jl b/lib/OptimizationBBO/src/OptimizationBBO.jl
@@ -37,13 +37,13 @@ function decompose_trace(opt::BlackBoxOptim.OptRunController, progress)
 end
 
 function __map_optimizer_args(prob::Optimization.OptimizationCache, opt::BBO;
-    callback = nothing,
-    maxiters::Union{Number, Nothing} = nothing,
-    maxtime::Union{Number, Nothing} = nothing,
-    abstol::Union{Number, Nothing} = nothing,
-    reltol::Union{Number, Nothing} = nothing,
-    verbose::Bool = false,
-    kwargs...)
+        callback = nothing,
+        maxiters::Union{Number, Nothing} = nothing,
+        maxtime::Union{Number, Nothing} = nothing,
+        abstol::Union{Number, Nothing} = nothing,
+        reltol::Union{Number, Nothing} = nothing,
+        verbose::Bool = false,
+        kwargs...)
     if !isnothing(reltol)
         @warn "common reltol is currently not used by $(opt)"
     end
@@ -78,30 +78,30 @@ function __map_optimizer_args(prob::Optimization.OptimizationCache, opt::BBO;
 end
 
 function SciMLBase.__solve(cache::Optimization.OptimizationCache{
-    F,
-    RC,
-    LB,
-    UB,
-    LC,
-    UC,
-    S,
-    O,
-    D,
-    P,
-    C,
+        F,
+        RC,
+        LB,
+        UB,
+        LC,
+        UC,
+        S,
+        O,
+        D,
+        P,
+        C,
 }) where {
-    F,
-    RC,
-    LB,
-    UB,
-    LC,
-    UC,
-    S,
-    O <:
-    BBO,
-    D,
-    P,
-    C,
+        F,
+        RC,
+        LB,
+        UB,
+        LC,
+        UC,
+        S,
+        O <:
+        BBO,
+        D,
+        P,
+        C,
 }
     local x, cur, state
 

diff --git a/lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl b/lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl
@@ -13,11 +13,11 @@ SciMLBase.allowscallback(::CMAEvolutionStrategyOpt) = false #looks like `logger`
 SciMLBase.supports_opt_cache_interface(opt::CMAEvolutionStrategyOpt) = true
 
 function __map_optimizer_args(prob::OptimizationCache, opt::CMAEvolutionStrategyOpt;
-    callback = nothing,
-    maxiters::Union{Number, Nothing} = nothing,
-    maxtime::Union{Number, Nothing} = nothing,
-    abstol::Union{Number, Nothing} = nothing,
-    reltol::Union{Number, Nothing} = nothing)
+        callback = nothing,
+        maxiters::Union{Number, Nothing} = nothing,
+        maxtime::Union{Number, Nothing} = nothing,
+        abstol::Union{Number, Nothing} = nothing,
+        reltol::Union{Number, Nothing} = nothing)
     if !isnothing(reltol)
         @warn "common reltol is currently not used by $(opt)"
     end
@@ -41,30 +41,30 @@ function __map_optimizer_args(prob::OptimizationCache, opt::CMAEvolutionStrategy
 end
 
 function SciMLBase.__solve(cache::OptimizationCache{
-    F,
-    RC,
-    LB,
-    UB,
-    LC,
-    UC,
-    S,
-    O,
-    D,
-    P,
-    C,
+        F,
+        RC,
+        LB,
+        UB,
+        LC,
+        UC,
+        S,
+        O,
+        D,
+        P,
+        C,
 }) where {
-    F,
-    RC,
-    LB,
-    UB,
-    LC,
-    UC,
-    S,
-    O <:
-    CMAEvolutionStrategyOpt,
-    D,
-    P,
-    C,
+        F,
+        RC,
+        LB,
+        UB,
+        LC,
+        UC,
+        S,
+        O <:
+        CMAEvolutionStrategyOpt,
+        D,
+        P,
+        C,
 }
     local x, cur, state