Merge branch 'master' into Vaibhavdixit02-patch-4

SciML · Dec 27, 2023 · 43abb68 · 43abb68
2 parents 7198ab9 + f800cf4
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diff --git a/.github/workflows/Downgrade.yml b/.github/workflows/Downgrade.yml
@@ -0,0 +1,53 @@
+name: Downgrade
+on:
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        version: ['1']
+        group:
+          - Core
+          - OptimizationBBO
+          - OptimizationCMAEvolutionStrategy
+          - OptimizationEvolutionary
+          - OptimizationFlux
+          - OptimizationGCMAES
+          - OptimizationMetaheuristics
+          - OptimizationMOI
+          - OptimizationMultistartOptimization
+          - OptimizationNLopt
+          #- OptimizationNonconvex
+          - OptimizationNOMAD
+          - OptimizationOptimJL
+          - OptimizationOptimisers
+          - OptimizationPRIMA
+          - OptimizationQuadDIRECT
+          - OptimizationSpeedMapping
+          - OptimizationPolyalgorithms
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: ${{ matrix.version }}
+      - uses: cjdoris/julia-downgrade-compat-action@v1
+#        if: ${{ matrix.version == '1.6' }}
+        with:
+          skip: Pkg,TOML
+      - uses: julia-actions/julia-buildpkg@v1
+      - if: ${{ matrix.group == 'OptimizationQuadDIRECT' }}
+        run: julia --project -e 'using Pkg; Pkg.Registry.add(RegistrySpec(url  = "https://github.com/HolyLab/HolyLabRegistry.git")); Pkg.add("QuadDIRECT")'
+      - uses: julia-actions/julia-runtest@v1
+        env:
+          GROUP: ${{ matrix.group }}
diff --git a/Project.toml b/Project.toml
@@ -14,7 +14,6 @@ Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
@@ -42,31 +41,30 @@ OptimizationTrackerExt = "Tracker"
 OptimizationZygoteExt = "Zygote"
 
 [compat]
-ADTypes = "0.1.5, 0.2"
-ArrayInterface = "6, 7"
-ConsoleProgressMonitor = "0.1"
-DocStringExtensions = "0.8, 0.9"
-Enzyme = "0.11.2"
-FiniteDiff = "2"
-ForwardDiff = "0.10"
-LinearAlgebra = "1"
-Logging = "1"
-LoggingExtras = "0.4, 0.5, 1"
-ModelingToolkit = "8"
+ADTypes = "0.2.5"
+ArrayInterface = "7.6"
+ConsoleProgressMonitor = "0.1.1"
+DocStringExtensions = "0.9"
+Enzyme = "0.11.9"
+FiniteDiff = "2.12"
+ForwardDiff = "0.10.26"
+LinearAlgebra = "1.9"
+Logging = "1.9"
+LoggingExtras = "0.4"
+ModelingToolkit = "8.74"
 Pkg = "1"
-Printf = "1"
+Printf = "1.9"
 ProgressLogging = "0.1"
-Reexport = "0.2, 1.0"
-Requires = "1.0"
-ReverseDiff = "1"
-SciMLBase = "1.79.0, 2"
-SparseArrays = "1"
-SparseDiffTools = "2"
+Reexport = "1.2"
+ReverseDiff = "1.14"
+SciMLBase = "2.11"
+SparseArrays = "1.9"
+SparseDiffTools = "2.14"
 SymbolicIndexingInterface = "0.3"
-Symbolics = "5"
+Symbolics = "5.12"
 TerminalLoggers = "0.1"
-Tracker = "0.2"
-Zygote = "0.6"
+Tracker = "0.2.29"
+Zygote = "0.6.67"
 julia = "1.9"
 
 [extras]

diff --git a/README.md b/README.md
@@ -4,7 +4,7 @@
 [![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/Optimization/stable/)
 
 [![codecov](https://codecov.io/gh/SciML/Optimization.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/Optimization.jl)
-[![Build Status](https://github.com/SciML/Optimization.jl/workflows/CI/badge.svg)](https://github.com/SciML/Optimization.jl/actions?query=workflow%3ACI)
+[![Build Status](https://github.com/SciML/Optimization.jl/workflows/CI/badge.svg)](https://github.com/SciML/Optimization.jl/actions/workflows/CI.yml?query=branch%3Amaster++)
 
 [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)

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...)