Merge pull request #708 from ArnoStrouwen/format

reapply formatter

Author: ChrisRackauckas

.JuliaFormatter.toml

@@ -1,2 +1,3 @@
 style = "sciml"
-format_markdown = true
+format_markdown = true
+format_docstrings = true

docs/pages.jl

@@ -4,18 +4,18 @@ pages = ["index.md",
         "tutorials/minibatch.md",
         "tutorials/symbolic.md",
         "tutorials/constraints.md",
-        "tutorials/linearandinteger.md",
+        "tutorials/linearandinteger.md"
     ],
     "Examples" => [
-        "examples/rosenbrock.md",
+        "examples/rosenbrock.md"
     ],
     "Basics" => [
         "API/optimization_problem.md",
         "API/optimization_function.md",
         "API/solve.md",
         "API/optimization_solution.md",
         "API/modelingtoolkit.md",
-        "API/FAQ.md",
+        "API/FAQ.md"
     ],
     "Optimizer Packages" => [
         "BlackBoxOptim.jl" => "optimization_packages/blackboxoptim.md",
@@ -33,6 +33,6 @@ pages = ["index.md",
         "PRIMA.jl" => "optimization_packages/prima.md",
         "Polyalgorithms.jl" => "optimization_packages/polyopt.md",
         "QuadDIRECT.jl" => "optimization_packages/quaddirect.md",
-        "SpeedMapping.jl" => "optimization_packages/speedmapping.md",
-    ],
+        "SpeedMapping.jl" => "optimization_packages/speedmapping.md"
+    ]
 ]

docs/src/optimization_packages/optim.md

@@ -58,6 +58,7 @@ For a more extensive documentation of all the algorithms and options, please con
   - [`Optim.IPNewton()`](https://julianlsolvers.github.io/Optim.jl/stable/#algo/ipnewton/)
 
       + `μ0` specifies the initial barrier penalty coefficient as either a number or `:auto`
+    
       + `show_linesearch` is an option to turn on linesearch verbosity.
       + Defaults:
 

docs/src/tutorials/linearandinteger.md

@@ -44,11 +44,11 @@ using Optimization, OptimizationMOI, ModelingToolkit, HiGHS, LinearAlgebra
 @variables m [bounds = (0.0, Inf)]
 
 cons = [u[1] + v[1] - w[1] ~ 150 # January
-    u[2] + v[2] - w[2] - 1.01u[1] + 1.003w[1] ~ 100 # February
-    u[3] + v[3] - w[3] - 1.01u[2] + 1.003w[2] ~ -200 # March
-    u[4] - w[4] - 1.02v[1] - 1.01u[3] + 1.003w[3] ~ 200 # April
-    u[5] - w[5] - 1.02v[2] - 1.01u[4] + 1.003w[4] ~ -50 # May
-    -m - 1.02v[3] - 1.01u[5] + 1.003w[5] ~ -300]
+        u[2] + v[2] - w[2] - 1.01u[1] + 1.003w[1] ~ 100 # February
+        u[3] + v[3] - w[3] - 1.01u[2] + 1.003w[2] ~ -200 # March
+        u[4] - w[4] - 1.02v[1] - 1.01u[3] + 1.003w[3] ~ 200 # April
+        u[5] - w[5] - 1.02v[2] - 1.01u[4] + 1.003w[4] ~ -50 # May
+        -m - 1.02v[3] - 1.01u[5] + 1.003w[5] ~ -300]
 
 @named optsys = OptimizationSystem(m, [u..., v..., w..., m], [], constraints = cons)
 optprob = OptimizationProblem(optsys,

docs/src/tutorials/minibatch.md

@@ -65,7 +65,8 @@ train_loader = Flux.Data.DataLoader((ode_data, t), batchsize = k)
 numEpochs = 300
 l1 = loss_adjoint(pp, train_loader.data[1], train_loader.data[2])[1]
 
-optfun = OptimizationFunction((θ, p, batch, time_batch) -> loss_adjoint(θ, batch,
+optfun = OptimizationFunction(
+    (θ, p, batch, time_batch) -> loss_adjoint(θ, batch,
         time_batch),
     Optimization.AutoZygote())
 optprob = OptimizationProblem(optfun, pp)

docs/src/tutorials/symbolic.md

@@ -34,9 +34,9 @@ our parameter values are and the initial conditions. This looks like:
 
 ```@example modelingtoolkit
 u0 = [x => 1.0
-    y => 2.0]
+      y => 2.0]
 p = [a => 6.0
-    b => 7.0]
+     b => 7.0]
 ```
 
 And now we solve.

ext/OptimizationFiniteDiffExt.jl

@@ -15,7 +15,8 @@ function Optimization.instantiate_function(f, x, adtype::AutoFiniteDiff, p,
 
     if f.grad === nothing
         gradcache = FD.GradientCache(x, x, adtype.fdtype)
-        grad = (res, θ, args...) -> FD.finite_difference_gradient!(res, x -> _f(x, args...),
+        grad = (res, θ, args...) -> FD.finite_difference_gradient!(
+            res, x -> _f(x, args...),
             θ, gradcache)
     else
         grad = (G, θ, args...) -> f.grad(G, θ, p, args...)
@@ -123,7 +124,8 @@ function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
 
     if f.grad === nothing
         gradcache = FD.GradientCache(cache.u0, cache.u0, adtype.fdtype)
-        grad = (res, θ, args...) -> FD.finite_difference_gradient!(res, x -> _f(x, args...),
+        grad = (res, θ, args...) -> FD.finite_difference_gradient!(
+            res, x -> _f(x, args...),
             θ, gradcache)
     else
         grad = (G, θ, args...) -> f.grad(G, θ, cache.p, args...)

ext/OptimizationForwardDiffExt.jl

@@ -65,7 +65,7 @@ function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
     if cons !== nothing && f.cons_h === nothing
         fncs = [(x) -> cons_oop(x)[i] for i in 1:num_cons]
         hess_config_cache = [ForwardDiff.HessianConfig(fncs[i], x,
-            ForwardDiff.Chunk{chunksize}())
+                                 ForwardDiff.Chunk{chunksize}())
                              for i in 1:num_cons]
         cons_h = function (res, θ)
             for i in 1:num_cons
@@ -143,7 +143,7 @@ function Optimization.instantiate_function(f::OptimizationFunction{true},
     if cons !== nothing && f.cons_h === nothing
         fncs = [(x) -> cons_oop(x)[i] for i in 1:num_cons]
         hess_config_cache = [ForwardDiff.HessianConfig(fncs[i], cache.u0,
-            ForwardDiff.Chunk{chunksize}())
+                                 ForwardDiff.Chunk{chunksize}())
                              for i in 1:num_cons]
         cons_h = function (res, θ)
             for i in 1:num_cons

ext/OptimizationMTKExt.jl

@@ -56,7 +56,8 @@ function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
         adtype::AutoModelingToolkit, num_cons = 0)
     p = isnothing(cache.p) ? SciMLBase.NullParameters() : cache.p
 
-    sys = complete(ModelingToolkit.modelingtoolkitize(OptimizationProblem(f, cache.u0, cache.p;
+    sys = complete(ModelingToolkit.modelingtoolkitize(OptimizationProblem(
+        f, cache.u0, cache.p;
         lcons = fill(0.0,
             num_cons),
         ucons = fill(0.0,

ext/OptimizationReverseDiffExt.jl

@@ -48,7 +48,7 @@ function Optimization.instantiate_function(f, x, adtype::AutoReverseDiff,
             xdual = ForwardDiff.Dual{
                 typeof(T),
                 eltype(x),
-                chunksize,
+                chunksize
             }.(x, Ref(ForwardDiff.Partials((ones(eltype(x), chunksize)...,))))
             h_tape = ReverseDiff.GradientTape(_f, xdual)
             htape = ReverseDiff.compile(h_tape)
@@ -118,9 +118,9 @@ function Optimization.instantiate_function(f, x, adtype::AutoReverseDiff,
             end
             gs = [x -> grad_cons(x, conshtapes[i]) for i in 1:num_cons]
             jaccfgs = [ForwardDiff.JacobianConfig(gs[i],
-                x,
-                ForwardDiff.Chunk{chunksize}(),
-                T) for i in 1:num_cons]
+                           x,
+                           ForwardDiff.Chunk{chunksize}(),
+                           T) for i in 1:num_cons]
             cons_h = function (res, θ)
                 for i in 1:num_cons
                     ForwardDiff.jacobian!(res[i], gs[i], θ, jaccfgs[i], Val{false}())
@@ -180,7 +180,7 @@ function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
             xdual = ForwardDiff.Dual{
                 typeof(T),
                 eltype(cache.u0),
-                chunksize,
+                chunksize
             }.(cache.u0, Ref(ForwardDiff.Partials((ones(eltype(cache.u0), chunksize)...,))))
             h_tape = ReverseDiff.GradientTape(_f, xdual)
             htape = ReverseDiff.compile(h_tape)
@@ -253,9 +253,9 @@ function Optimization.instantiate_function(f, cache::Optimization.ReInitCache,
             end
             gs = [x -> grad_cons(x, conshtapes[i]) for i in 1:num_cons]
             jaccfgs = [ForwardDiff.JacobianConfig(gs[i],
-                cache.u0,
-                ForwardDiff.Chunk{chunksize}(),
-                T) for i in 1:num_cons]
+                           cache.u0,
+                           ForwardDiff.Chunk{chunksize}(),
+                           T) for i in 1:num_cons]
             cons_h = function (res, θ)
                 for i in 1:num_cons
                     ForwardDiff.jacobian!(res[i], gs[i], θ, jaccfgs[i], Val{false}())