Updates

Author: ParamThakkar123

lib/OptimizationLBFGS/test/runtests.jl

@@ -7,49 +7,51 @@ using MLUtils
 using LBFGSB
 using Test
 
-x0 = zeros(2)
-rosenbrock(x, p = nothing) = (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2
-l1 = rosenbrock(x0)
-
-optf = OptimizationFunction(rosenbrock, OptimizationBase.AutoForwardDiff())
-prob = OptimizationProblem(optf, x0)
-@time res = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 100)
-@test res.retcode == ReturnCode.Success
-
-prob = OptimizationProblem(optf, x0, lb = [-1.0, -1.0], ub = [1.0, 1.0])
-@time res = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 100)
-@test res.retcode == ReturnCode.Success
-
-function con2_c(res, x, p)
-    res .= [x[1]^2 + x[2]^2, (x[2] * sin(x[1]) + x[1]) - 5]
-end
-
-optf = OptimizationFunction(rosenbrock, OptimizationBase.AutoZygote(), cons = con2_c)
-prob = OptimizationProblem(optf, x0, lcons = [1.0, -Inf],
-    ucons = [1.0, 0.0], lb = [-1.0, -1.0],
-    ub = [1.0, 1.0])
-@time res = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 100)
-@test res.retcode == SciMLBase.ReturnCode.Success
-
-x0 = (-pi):0.001:pi
-y0 = sin.(x0)
-data = MLUtils.DataLoader((x0, y0), batchsize = 126)
-function loss(coeffs, data)
-    ypred = [evalpoly(data[1][i], coeffs) for i in eachindex(data[1])]
-    return sum(abs2, ypred .- data[2])
-end
-
-function cons1(res, coeffs, p = nothing)
-    res[1] = coeffs[1] * coeffs[5] - 1
-    return nothing
-end
-
-optf = OptimizationFunction(loss, AutoSparseForwardDiff(), cons = cons1)
-callback = (st, l) -> (@show l; return false)
-
-initpars = rand(5)
-l0 = optf(initpars, (x0, y0))
-prob = OptimizationProblem(optf, initpars, (x0, y0), lcons = [-Inf], ucons = [0.5],
-    lb = [-10.0, -10.0, -10.0, -10.0, -10.0], ub = [10.0, 10.0, 10.0, 10.0, 10.0])
-opt1 = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 1000, callback = callback)
-@test opt1.objective < l0
+@testset "OptimizationLBFGS.jl" begin
+    x0 = zeros(2)
+    rosenbrock(x, p = nothing) = (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2
+    l1 = rosenbrock(x0)
+
+    optf = OptimizationFunction(rosenbrock, OptimizationBase.AutoForwardDiff())
+    prob = OptimizationProblem(optf, x0)
+    @time res = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 100)
+    @test res.retcode == ReturnCode.Success
+
+    prob = OptimizationProblem(optf, x0, lb = [-1.0, -1.0], ub = [1.0, 1.0])
+    @time res = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 100)
+    @test res.retcode == ReturnCode.Success
+
+    function con2_c(res, x, p)
+        res .= [x[1]^2 + x[2]^2, (x[2] * sin(x[1]) + x[1]) - 5]
+    end
+
+    optf = OptimizationFunction(rosenbrock, OptimizationBase.AutoZygote(), cons = con2_c)
+    prob = OptimizationProblem(optf, x0, lcons = [1.0, -Inf],
+        ucons = [1.0, 0.0], lb = [-1.0, -1.0],
+        ub = [1.0, 1.0])
+    @time res = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 100)
+    @test res.retcode == SciMLBase.ReturnCode.Success
+
+    x0 = (-pi):0.001:pi
+    y0 = sin.(x0)
+    data = MLUtils.DataLoader((x0, y0), batchsize = 126)
+    function loss(coeffs, data)
+        ypred = [evalpoly(data[1][i], coeffs) for i in eachindex(data[1])]
+        return sum(abs2, ypred .- data[2])
+    end
+
+    function cons1(res, coeffs, p = nothing)
+        res[1] = coeffs[1] * coeffs[5] - 1
+        return nothing
+    end
+
+    optf = OptimizationFunction(loss, AutoSparseForwardDiff(), cons = cons1)
+    callback = (st, l) -> (@show l; return false)
+
+    initpars = rand(5)
+    l0 = optf(initpars, (x0, y0))
+    prob = OptimizationProblem(optf, initpars, (x0, y0), lcons = [-Inf], ucons = [0.5],
+        lb = [-10.0, -10.0, -10.0, -10.0, -10.0], ub = [10.0, 10.0, 10.0, 10.0, 10.0])
+    opt1 = solve(prob, OptimizationLBFGS.LBFGS(), maxiters = 1000, callback = callback)
+    @test opt1.objective < l0
+end