formatting

Author: mohdibntarek

src/NonconvexPercival.jl

@@ -12,7 +12,7 @@ import NonconvexCore: Workspace, reset!, optimize!
 struct PercivalAlg <: AbstractOptimizer end
 const AugLag = PercivalAlg
 
-struct PercivalOptions{T <: NamedTuple}
+struct PercivalOptions{T<:NamedTuple}
     nt::T
 end
 function PercivalOptions(; first_order = true, memory = 5, kwargs...)
@@ -25,7 +25,13 @@ function PercivalOptions(; first_order = true, memory = 5, kwargs...)
 end
 const AugLagOptions = PercivalOptions
 
-mutable struct PercivalWorkspace{TM <: VecModel, TP <: Percival.NLPModels.AbstractNLPModel, TX <: AbstractVector, TO <: PercivalOptions, TC <: Base.RefValue{Int}} <: Workspace
+mutable struct PercivalWorkspace{
+    TM<:VecModel,
+    TP<:Percival.NLPModels.AbstractNLPModel,
+    TX<:AbstractVector,
+    TO<:PercivalOptions,
+    TC<:Base.RefValue{Int},
+} <: Workspace
     model::TM
     problem::TP
     x0::TX
@@ -41,7 +47,7 @@ function PercivalWorkspace(
     problem, counter = get_percival_problem(model, copy(x0))
     return PercivalWorkspace(model, problem, copy(x0), options, counter)
 end
-struct PercivalResult{TM1, TM2, TP, TR, TF} <: AbstractResult
+struct PercivalResult{TM1,TM2,TP,TR,TF} <: AbstractResult
     minimizer::TM1
     minimum::TM2
     problem::TP

test/runtests.jl

@@ -110,17 +110,14 @@ end
     component_pdf(x::Real, mu::Real, tau::Real) =
         tau * exp(-tau^2 * (x - mu)^2 / 2) / sqrt(2π)
     data = randn(StableRNG(123), 40)
-    loss(params::AbstractVector) = -sum(log,
-        (1 - params[1]) * component_pdf(x, 0, 1)
-        +    params[1]  * component_pdf(x, params[2], params[3])
-        for x in data
-    )
+    loss(params::AbstractVector) =
+        -sum(
+            log,
+            (1 - params[1]) * component_pdf(x, 0, 1) +
+            params[1] * component_pdf(x, params[2], params[3]) for x in data
+        )
     model = Model(loss)
-    addvar!(model, [0, -Inf, 0], [1, Inf, Inf], init=[0.1, 0, 1])
-    res = optimize(model, AugLag(), [0.1, -3, 1], options=AugLagOptions())
-    @test res.minimizer == [
-        0.0,
-        -2.3140286844577305,
-        0.06904076799516767,
-    ]
+    addvar!(model, [0, -Inf, 0], [1, Inf, Inf], init = [0.1, 0, 1])
+    res = optimize(model, AugLag(), [0.1, -3, 1], options = AugLagOptions())
+    @test res.minimizer == [0.0, -2.3140286844577305, 0.06904076799516767]
 end