Unflatten initial_x in GenericResult

src/algorithms/common.jl

@@ -184,6 +184,22 @@ An abstract type that stores optimization result.
 """
 abstract type AbstractResult end
 
+"""
+    restore_input!
+    Some fields of `AbstractResult` should be flattened after optimization, use this method whenever before returning it to frontend
+"""
+function restore_input!(r::AbstractResult, unflatten)
+    if :minimizer in fieldnames(typeof(r))
+        @set! r.minimizer = unflatten(r.minimizer)
+    end
+    if :initial_x in fieldnames(typeof(r))
+        @set! r.initial_x = unflatten(r.initial_x)
+    end
+    return r
+end
+
+
+
 """
     GenericResult
 
@@ -212,6 +228,7 @@ end
 
 abstract type AbstractModel end
 
+
 """
 ```
 optimize(
@@ -224,6 +241,7 @@ optimize(
     callback::Function = plot_trace ? LazyPlottingCallback() : NoCallback(),
     kwargs...,
 )
+
 ```
 
 Optimizes `model` using the algorithm `optimizer`, e.g. an instance of [`MMA87`](@ref) or [`MMA02`](@ref). `x0` is the initial solution. The keyword arguments are:
@@ -237,7 +255,7 @@ Optimizes `model` using the algorithm `optimizer`, e.g. an instance of [`MMA87`]
 function optimize(model::AbstractModel, optimizer::AbstractOptimizer, x0, args...; kwargs...)
     _model, _x0, unflatten = tovecmodel(model, x0)
     r = optimize(_model, optimizer, _x0, args...; kwargs...)
-    return @set r.minimizer = unflatten(r.minimizer)
+    return restore_input!(r, unflatten)
 end
 
 """
@@ -246,7 +264,7 @@ end
  function optimize(model::AbstractModel, optimizer::AbstractOptimizer, args...; kwargs...)
     _model, _, unflatten = tovecmodel(model)
     r = optimize(_model, optimizer, args...; kwargs...)
-    return @set r.minimizer = unflatten(r.minimizer)
+    return restore_input!(r, unflatten)
 end
 
 """

test/mma.jl

@@ -98,3 +98,36 @@ end
         @test norm(r.minimizer - [1/3, 8/27]) < 1e-6
     end
 end
+
+# Test unflatten minimizer and initial_x
+@testset "Unflatten initial_x" begin
+    function _f((x, _))
+        sqrt(x[2])
+    end
+    function _g((x, _), a, b)
+        (a*x[1] + b)^3 - x[2]
+    end
+
+    _options = MMAOptions(
+        tol = Tolerance(kkt = 1e-6, f = 0.0),
+        s_init = 0.1,
+        maxiter = 1
+    )
+
+    m = Model(_f)
+    addvar!(m, [(0.0, 0.0), (0.0, 0.0)], [(10.0, Inf), (10.0, Inf)])
+
+    function eqc1(x)
+        _g(x, 2, 0)
+    end
+    function eqc2(x)
+        _g(x, -1, 1)
+    end
+    add_ineq_constraint!(m, eqc1)
+    add_ineq_constraint!(m, eqc2)
+
+    x0 = [(0.0, 0.0), (0.0, 0.0)]
+    r = Nonconvex.optimize(m, MMA02(), x0, options = _options, convcriteria = KKTCriteria())
+    @test size(r.minimizer) == (2,)
+    @test size(r.initial_x) == (2,)
+end