More lower iterations

test/likelihood.jl

@@ -19,12 +19,12 @@ end
 aggregate_data = convert(Array, VectorOfArray([generate_data(sol, t) for i in 1:100]))
 
 distributions = [fit_mle(Normal, aggregate_data[i, j, :]) for i in 1:2, j in 1:200]
-obj = build_loss_objective(prob1, Tsit5(), LogLikeLoss(t, distributions), maxiters = 10000,
+obj = build_loss_objective(prob1, Tsit5(), LogLikeLoss(t, distributions), maxiters = 1000,
     verbose = false)
 
 optprob = Optimization.OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5],
     ub = [5.0, 5.0])
-result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 11e3)
+result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 1e3)
 @test result.original.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
 
 data_distributions = [fit_mle(Normal, aggregate_data[i, j, :]) for i in 1:2, j in 1:200]
@@ -33,11 +33,11 @@ diff_distributions = [fit_mle(Normal,
                       for j in 2:200, i in 1:2]
 obj = build_loss_objective(prob1, Tsit5(),
     LogLikeLoss(t, data_distributions, diff_distributions),
-    Optimization.AutoForwardDiff(), maxiters = 10000,
+    Optimization.AutoForwardDiff(), maxiters = 1000,
     verbose = false)
 optprob = Optimization.OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5],
     ub = [5.0, 5.0])
-result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 11e3)
+result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 1e3)
 @test result.original.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
 
 data_distributions = [fit_mle(Normal, aggregate_data[i, j, :]) for i in 1:2, j in 1:200]
@@ -46,14 +46,14 @@ diff_distributions = [fit_mle(Normal,
                       for j in 2:200, i in 1:2]
 obj = build_loss_objective(prob1, Tsit5(),
     LogLikeLoss(t, data_distributions, diff_distributions, 0.3),
-    Optimization.AutoForwardDiff(), maxiters = 10000,
+    Optimization.AutoForwardDiff(), maxiters = 1000,
     verbose = false)
 optprob = Optimization.OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5],
     ub = [5.0, 5.0])
-result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 11e3)
+result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 1e3)
 @test result.u≈[1.5, 1.0] atol=1e-1
 using OptimizationBBO.BlackBoxOptim
-result = bboptimize(obj, SearchRange = [(0.5, 5.0), (0.5, 5.0)], MaxSteps = 11e3)
+result = bboptimize(obj, SearchRange = [(0.5, 5.0), (0.5, 5.0)], MaxSteps = 1e3)
 @test result.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
 
 distributions = [fit_mle(MvNormal, aggregate_data[:, j, :]) for j in 1:200]
@@ -63,9 +63,9 @@ diff_distributions = [fit_mle(MvNormal,
 priors = [Truncated(Normal(1.5, 0.1), 0, 2), Truncated(Normal(1.0, 0.1), 0, 1.5)]
 obj = build_loss_objective(prob1, Tsit5(),
     LogLikeLoss(t, distributions, diff_distributions),
-    Optimization.AutoForwardDiff(), maxiters = 10000,
+    Optimization.AutoForwardDiff(), maxiters = 1000,
     verbose = false, priors = priors)
 optprob = Optimization.OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5],
     ub = [5.0, 5.0])
-result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 11e3)
+result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 1e3)
 @test result.u≈[1.5, 1.0] atol=1e-1

test/multiple_shooting_objective_test.jl

@@ -18,7 +18,7 @@ ms_obj = multiple_shooting_objective(ms_prob, Tsit5(), L2Loss(t, data),
     Optimization.AutoZygote();
     discontinuity_weight = 1.0, abstol = 1e-12,
     reltol = 1e-12)
-result = bboptimize(ms_obj; SearchRange = bound, MaxSteps = 21e3)
+result = bboptimize(ms_obj; SearchRange = bound, MaxSteps = 1e3)
 @test result.archive_output.best_candidate[(end - 1):end]≈[1.5, 1.0] atol=2e-1
 
 priors = [Truncated(Normal(1.5, 0.5), 0, 2), Truncated(Normal(1.0, 0.5), 0, 1.5)]

test/tests_on_odes/blackboxoptim_test.jl

@@ -2,19 +2,19 @@ using BlackBoxOptim
 
 println("Use BlackBoxOptim to fit the parameter")
 cost_function = build_loss_objective(prob1, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 bound1 = Tuple{Float64, Float64}[(1, 2)]
-result = bboptimize(cost_function; SearchRange = bound1, MaxSteps = 5e3)
+result = bboptimize(cost_function; SearchRange = bound1, MaxSteps = 1e3)
 @test result.archive_output.best_candidate[1]≈1.5 atol=3e-1
 
 cost_function = build_loss_objective(prob2, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 bound2 = Tuple{Float64, Float64}[(1, 2), (2, 4)]
-result = bboptimize(cost_function; SearchRange = bound2, MaxSteps = 5e3)
+result = bboptimize(cost_function; SearchRange = bound2, MaxSteps = 1e3)
 @test result.archive_output.best_candidate≈[1.5; 3.0] atol=3e-1
 
 cost_function = build_loss_objective(prob3, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 bound3 = Tuple{Float64, Float64}[(1, 2), (0, 2), (2, 4), (0, 2)]
-result = bboptimize(cost_function; SearchRange = bound3, MaxSteps = 5e3)
+result = bboptimize(cost_function; SearchRange = bound3, MaxSteps = 11e3)
 @test result.archive_output.best_candidate≈[1.5; 1.0; 3.0; 1.0] atol=5e-1

test/tests_on_odes/genetic_algorithm_test.jl

@@ -14,7 +14,7 @@ println("Use Genetic Algorithm to fit the parameter")
 #                 Floating number specifies fraction of population.
 
 cost_function = build_loss_objective(prob1, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 N = 1
 result, fitness, cnt = ga(cost_function, N;
     initPopulation = Float64[1.2],
@@ -26,7 +26,7 @@ result, fitness, cnt = ga(cost_function, N;
 @test result[1]≈1.5 atol=3e-1
 
 cost_function = build_loss_objective(prob2, Tsit5(), L2Loss(t, data),
-    maxiters = 10000)
+    maxiters = 1000)
 N = 2
 result, fitness, cnt = ga(cost_function, N;
     initPopulation = Float64[1.2, 2.8],
@@ -38,7 +38,7 @@ result, fitness, cnt = ga(cost_function, N;
 @test result≈[1.5; 3.0] atol=3e-1
 
 cost_function = build_loss_objective(prob3, Tsit5(), L2Loss(t, data),
-    maxiters = 10000)
+    maxiters = 1000)
 N = 4
 result, fitness, cnt = ga(cost_function, N;
     initPopulation = Float64[1.3, 0.8, 2.8, 1.2],

test/tests_on_odes/l2_colloc_grad_test.jl

@@ -2,30 +2,30 @@ weight = 1.0e-6
 
 cost_function = build_loss_objective(prob1, Tsit5(),
     L2Loss(t, data, colloc_grad = colloc_grad(t, data)),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 result = Optim.optimize(cost_function, 1.0, 2.0)
 @test result.minimizer≈1.5 atol=3e-1
 
 cost_function = build_loss_objective(prob2, Tsit5(),
     L2Loss(t, data,
         differ_weight = weight, data_weight = weight,
         colloc_grad = colloc_grad(t, data)),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 result = Optim.optimize(cost_function, [1.3, 2.8], Optim.BFGS())
 @test result.minimizer≈[1.5; 3.0] atol=3e-1
 
 cost_function = build_loss_objective(prob3, Tsit5(),
     L2Loss(t, data,
         differ_weight = weight,
         colloc_grad = colloc_grad(t, data)),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 result = Optim.optimize(cost_function, [1.4, 0.9, 2.9, 1.2], Optim.BFGS())
 @test result.minimizer≈[1.5, 1.0, 3.0, 1.0] atol=3e-1
 
 cost_function = build_loss_objective(prob1, Tsit5(),
     L2Loss(t, data,
         data_weight = weight,
         colloc_grad = colloc_grad(t, data)),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 result = Optim.optimize(cost_function, 1.0, 2)
 @test result.minimizer≈1.5 atol=3e-1

test/tests_on_odes/l2loss_test.jl

@@ -3,27 +3,27 @@ using BlackBoxOptim, Optim
 cost_function = build_loss_objective(prob1, Tsit5(), L2Loss(t, data),
     maxiters = 10000, verbose = false)
 bound1 = Tuple{Float64, Float64}[(1, 2)]
-result = bboptimize(cost_function; SearchRange = bound1, MaxSteps = 11e3)
+result = bboptimize(cost_function; SearchRange = bound1, MaxSteps = 1e3)
 @test result.archive_output.best_candidate[1]≈1.5 atol=3e-1
 
 cost_function = build_loss_objective(prob2, Tsit5(),
     L2Loss(t, data, differ_weight = nothing,
         data_weight = 1.0),
     maxiters = 10000, verbose = false)
 bound2 = Tuple{Float64, Float64}[(1, 2), (1, 4)]
-result = bboptimize(cost_function; SearchRange = bound2, MaxSteps = 11e3)
+result = bboptimize(cost_function; SearchRange = bound2, MaxSteps = 1e3)
 @test result.archive_output.best_candidate≈[1.5; 3.0] atol=3e-1
 
 cost_function = build_loss_objective(prob3, Tsit5(), L2Loss(t, data, differ_weight = 10),
     maxiters = 10000, verbose = false)
 bound3 = Tuple{Float64, Float64}[(1, 2), (0, 2), (2, 4), (0, 2)]
-result = bboptimize(cost_function; SearchRange = bound3, MaxSteps = 11e3)
+result = bboptimize(cost_function; SearchRange = bound3, MaxSteps = 1e3)
 @test result.archive_output.best_candidate≈[1.5; 1.0; 3.0; 1.0] atol=5e-1
 
 cost_function = build_loss_objective(prob3, Tsit5(),
     L2Loss(t, data, differ_weight = 0.3,
         data_weight = 0.7),
     maxiters = 10000, verbose = false)
 bound3 = Tuple{Float64, Float64}[(1, 2), (0, 2), (1, 4), (0, 2)]
-result = bboptimize(cost_function; SearchRange = bound3, MaxSteps = 11e3)
+result = bboptimize(cost_function; SearchRange = bound3, MaxSteps = 1e3)
 @test result.archive_output.best_candidate≈[1.5; 1.0; 3.0; 1.0] atol=5e-1

test/tests_on_odes/nlopt_test.jl

@@ -3,7 +3,7 @@ using OptimizationNLopt, Zygote
 println("Use NLOpt to fit the parameter")
 
 obj = build_loss_objective(prob1, Tsit5(), L2Loss(t, data), Optimization.AutoZygote(),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 
 opt = Opt(:LN_COBYLA, 1)
 optprob = OptimizationNLopt.OptimizationProblem(obj, [1.4])
@@ -14,25 +14,25 @@ opt = Opt(:GN_ESCH, 1)
 lower_bounds!(opt, [1.0])
 upper_bounds!(opt, [3.0])
 xtol_rel!(opt, 1e-3)
-maxeval!(opt, 10000)
+maxeval!(opt, 1000)
 res = solve(optprob, opt)
 @test res.u[1]≈1.5 atol=1e-1
 
 opt = Opt(:GN_ISRES, 1)
 lower_bounds!(opt, [1.0])
 upper_bounds!(opt, [3.0])
 xtol_rel!(opt, 1e-4)
-maxeval!(opt, 100 - 000)
+maxeval!(opt, 1000)
 res = solve(optprob, opt)
 @test res.u[1]≈1.5 atol=1e-1
 
 # test differentiation
 
 obj = build_loss_objective(prob1, Tsit5(), L2Loss(t, data), Optimization.AutoForwardDiff();
-    maxiters = 10000) #zygote behaves weirdly here
+    maxiters = 1000) #zygote behaves weirdly here
 opt = Opt(:LD_MMA, 1)
 xtol_rel!(opt, 1e-3)
-maxeval!(opt, 10000)
+maxeval!(opt, 1000)
 optprob = OptimizationNLopt.OptimizationProblem(obj, [1.3])
 res = solve(optprob, opt)
 @test res.u[1]≈1.5 atol=1e-1

test/tests_on_odes/optim_test.jl

@@ -1,6 +1,6 @@
 using Optim, Random
 obj = build_loss_objective(prob1, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 
 ### Optim Method
 

test/tests_on_odes/regularization_test.jl

@@ -2,32 +2,32 @@ using PenaltyFunctions, OptimizationOptimJL, LinearAlgebra, SciMLSensitivity
 
 cost_function_1 = build_loss_objective(prob1, Tsit5(), L2Loss(t, data),
     Optimization.AutoZygote(),
-    Regularization(0.6, L2Penalty()), maxiters = 10000,
+    Regularization(0.6, L2Penalty()), maxiters = 1000,
     verbose = false, abstol = 1e-8, reltol = 1e-8)
 cost_function_2 = build_loss_objective(prob2, Tsit5(), L2Loss(t, data),
     Optimization.AutoZygote(),
     Regularization(0.1,
         MahalanobisPenalty(Matrix(1.0I, 2, 2))),
     verbose = false,
     abstol = 1e-8, reltol = 1e-8,
-    maxiters = 10000)
+    maxiters = 1000)
 cost_function_3 = build_loss_objective(prob3, Tsit5(), L2Loss(t, data),
     Optimization.AutoZygote(),
     Regularization(0.1,
         MahalanobisPenalty(Matrix(1.0I, 4, 4))),
     verbose = false,
     abstol = 1e-8, reltol = 1e-8,
-    maxiters = 10000)
+    maxiters = 1000)
 
 println("Use Optim BFGS to fit the parameter")
 optprob = Optimization.OptimizationProblem(cost_function_1, [1.0])
-result = solve(optprob, Optim.LBFGS())
+result = solve(optprob, Optim.BFGS())
 @test result.u[1]≈1.5 atol=3e-1
 
 optprob = Optimization.OptimizationProblem(cost_function_2, [1.2, 2.7])
-result = solve(optprob, Optim.LBFGS())
+result = solve(optprob, Optim.BFGS())
 @test result.minimizer≈[1.5; 3.0] atol=3e-1
 
 optprob = Optimization.OptimizationProblem(cost_function_3, [1.3, 0.8, 2.8, 1.2])
-result = solve(optprob, Optim.LBFGS())
+result = solve(optprob, Optim.BFGS())
 @test result.minimizer≈[1.5; 1.0; 3.0; 1.0] atol=5e-1

test/tests_on_odes/weighted_loss_test.jl

@@ -22,7 +22,7 @@ weighted_data = original_solution_matrix_form + error
 weighted_cost_function = build_loss_objective(prob1, Tsit5(),
     L2Loss(t, weighted_data,
         data_weight = weight),
-    maxiters = 10000, verbose = false)
+    maxiters = 1000, verbose = false)
 opt = Opt(:LN_COBYLA, 1)
 min_objective!(opt, weighted_cost_function)
 (minf, minx, ret) = NLopt.optimize(opt, [1.3])