🤖 Format .jl files (#252)

Co-authored-by: tmigot <[email protected]>
JuliaSmoothOptimizers · Jun 20, 2024 · 6ea9186 · 6ea9186
1 parent 86f6ad6
commit 6ea9186
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Showing 7 changed files with 32 additions and 19 deletions.
diff --git a/benchmark/hessian/benchmarks_coloring.jl b/benchmark/hessian/benchmarks_coloring.jl
@@ -9,13 +9,21 @@ using ForwardDiff, SparseConnectivityTracer, SparseMatrixColorings, Symbolics
 
 include("additional_backends.jl")
 
-data_types = [Float64] 
+data_types = [Float64]
 
 benchmark_list = [:optimized]
 
 benchmarked_hess_coloring_backend = Dict(
   "sparse" => ADNLPModels.SparseADHessian,
-  "sparse_symbolics" => (nvar, f, ncon, c!; kwargs...) -> ADNLPModels.SparseADHessian(nvar, f, ncon, c!; detector = SymbolicsSparsityDetector(), kwargs...),
+  "sparse_symbolics" =>
+    (nvar, f, ncon, c!; kwargs...) -> ADNLPModels.SparseADHessian(
+      nvar,
+      f,
+      ncon,
+      c!;
+      detector = SymbolicsSparsityDetector(),
+      kwargs...,
+    ),
   # add ColPack?
 )
 get_backend_list(::Val{:optimized}) = keys(benchmarked_hess_coloring_backend)
@@ -42,7 +50,8 @@ for f in benchmark_list
           n = eval(Meta.parse("OptimizationProblems.get_" * pb * "_nvar(n = $(nscal))"))
           m = eval(Meta.parse("OptimizationProblems.get_" * pb * "_ncon(n = $(nscal))"))
           @info " $(pb): $T with $n vars and $m cons"
-          SUITE["$(fun)"][f][T][s][b][pb] = @benchmarkable set_adnlp($pb, $(name_backend), $backend, $nscal, $T)
+          SUITE["$(fun)"][f][T][s][b][pb] =
+            @benchmarkable set_adnlp($pb, $(name_backend), $backend, $nscal, $T)
         end
       end
     end

diff --git a/benchmark/hessian/benchmarks_hessian.jl b/benchmark/hessian/benchmarks_hessian.jl
@@ -12,9 +12,7 @@ data_types = [Float32, Float64]
 
 benchmark_list = [:optimized]
 
-benchmarked_hessian_backend = Dict(
-  "sparse" => ADNLPModels.SparseADHessian,
-)
+benchmarked_hessian_backend = Dict("sparse" => ADNLPModels.SparseADHessian)
 get_backend_list(::Val{:optimized}) = keys(benchmarked_hessian_backend)
 get_backend(::Val{:optimized}, b::String) = benchmarked_hessian_backend[b]
 

diff --git a/benchmark/hessian/benchmarks_hessian_lagrangian.jl b/benchmark/hessian/benchmarks_hessian_lagrangian.jl
@@ -12,9 +12,7 @@ data_types = [Float32, Float64]
 
 benchmark_list = [:optimized]
 
-benchmarked_hessian_backend = Dict(
-  "sparse" => ADNLPModels.SparseADHessian,
-)
+benchmarked_hessian_backend = Dict("sparse" => ADNLPModels.SparseADHessian)
 get_backend_list(::Val{:optimized}) = keys(benchmarked_hessian_backend)
 get_backend(::Val{:optimized}, b::String) = benchmarked_hessian_backend[b]
 

diff --git a/benchmark/jacobian/benchmarks_coloring.jl b/benchmark/jacobian/benchmarks_coloring.jl
@@ -9,13 +9,21 @@ using ForwardDiff, SparseConnectivityTracer, SparseMatrixColorings, Symbolics
 
 include("additional_backends.jl")
 
-data_types = [Float64] 
+data_types = [Float64]
 
 benchmark_list = [:optimized]
 
 benchmarked_jac_coloring_backend = Dict(
   "sparse" => ADNLPModels.SparseADJacobian,
-  "sparse_symbolics" => (nvar, f, ncon, c!; kwargs...) -> ADNLPModels.SparseADJacobian(nvar, f, ncon, c!; detector = SymbolicsSparsityDetector(), kwargs...),
+  "sparse_symbolics" =>
+    (nvar, f, ncon, c!; kwargs...) -> ADNLPModels.SparseADJacobian(
+      nvar,
+      f,
+      ncon,
+      c!;
+      detector = SymbolicsSparsityDetector(),
+      kwargs...,
+    ),
   # add ColPack?
 )
 get_backend_list(::Val{:optimized}) = keys(benchmarked_jac_coloring_backend)
@@ -42,7 +50,8 @@ for f in benchmark_list
           n = eval(Meta.parse("OptimizationProblems.get_" * pb * "_nvar(n = $(nscal))"))
           m = eval(Meta.parse("OptimizationProblems.get_" * pb * "_ncon(n = $(nscal))"))
           @info " $(pb): $T with $n vars and $m cons"
-          SUITE["$(fun)"][f][T][s][b][pb] = @benchmarkable set_adnlp($pb, $(name_backend), $backend, $nscal, $T)
+          SUITE["$(fun)"][f][T][s][b][pb] =
+            @benchmarkable set_adnlp($pb, $(name_backend), $backend, $nscal, $T)
         end
       end
     end

diff --git a/benchmark/jacobian/benchmarks_jacobian.jl b/benchmark/jacobian/benchmarks_jacobian.jl
@@ -12,9 +12,7 @@ data_types = [Float32, Float64]
 
 benchmark_list = [:optimized]
 
-benchmarked_jacobian_backend = Dict(
-  "sparse" => ADNLPModels.SparseADJacobian,
-)
+benchmarked_jacobian_backend = Dict("sparse" => ADNLPModels.SparseADJacobian)
 get_backend_list(::Val{:optimized}) = keys(benchmarked_jacobian_backend)
 get_backend(::Val{:optimized}, b::String) = benchmarked_jacobian_backend[b]
 

diff --git a/benchmark/jacobian/benchmarks_jacobian_residual.jl b/benchmark/jacobian/benchmarks_jacobian_residual.jl
@@ -12,9 +12,7 @@ data_types = [Float32, Float64]
 
 benchmark_list = [:optimized]
 
-benchmarked_jacobian_backend = Dict(
-  "sparse" => ADNLPModels.SparseADJacobian,
-)
+benchmarked_jacobian_backend = Dict("sparse" => ADNLPModels.SparseADJacobian)
 get_backend_list(::Val{:optimized}) = keys(benchmarked_jacobian_backend)
 get_backend(::Val{:optimized}, b::String) = benchmarked_jacobian_backend[b]
 

diff --git a/benchmark/problems_sets.jl b/benchmark/problems_sets.jl
@@ -8,7 +8,10 @@ all_problems = meta[meta.nvar .> 5, :name] # all problems with ≥ 5 variables
 all_problems = setdiff(all_problems, scalable_problems) # avoid duplicate problems
 
 # all scalable least squares problems with ≥ 5 variables
-scalable_nls_problems = meta[(meta.variable_nvar .== true) .&& (meta.nvar .> 5) .&& (meta.objtype .== :least_squares), :name]
+scalable_nls_problems = meta[
+  (meta.variable_nvar .== true) .&& (meta.nvar .> 5) .&& (meta.objtype .== :least_squares),
+  :name,
+]
 
 all_cons_problems = meta[(meta.nvar .> 5) .&& (meta.ncon .> 5), :name] # all problems with ≥ 5 variables
 scalable_cons_problems = meta[(meta.variable_nvar .== true) .&& (meta.ncon .> 5), :name] # problems that are scalable