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* Update maximize.jl * Update maximize.jl * Update maximize.jl
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@testset "maximization wrapper" begin | ||
@testset "univariate" begin | ||
resmax = maximize(x->x^3, -1, 9) | ||
resmin = optimize(x->-x^3, -1, 9) | ||
resmax = maximize(x -> x^3, -1, 9) | ||
resmin = optimize(x -> -x^3, -1, 9) | ||
@test Optim.maximum(resmax) == -Optim.minimum(resmin) | ||
@test resmax.res.minimum == resmin.minimum | ||
for meth in (Brent(), GoldenSection()) | ||
resmax = maximize(x->x^3, -1, 9, meth) | ||
resmin = optimize(x->-x^3, -1, 9, meth) | ||
resmax = maximize(x -> x^3, -1, 9, meth) | ||
resmin = optimize(x -> -x^3, -1, 9, meth) | ||
@test Optim.maximum(resmax) == -Optim.minimum(resmin) | ||
@test resmax.res.minimum == resmin.minimum | ||
end | ||
end | ||
@testset "multivariate" begin | ||
resmax = maximize(x->x[1]^3+x[2]^2, [3.0, 0.0]) | ||
resmin = optimize(x->-x[1]^3-x[2]^2, [3.0, 0.0]) | ||
resmax = maximize(x -> x[1]^3 + x[2]^2, [3.0, 0.0]) | ||
resmin = optimize(x -> -x[1]^3 - x[2]^2, [3.0, 0.0]) | ||
@test Optim.maximum(resmax) == -Optim.minimum(resmin) | ||
@test resmax.res.minimum == resmin.minimum | ||
for meth in (NelderMead(), BFGS(), LBFGS(), GradientDescent(), Newton(), NewtonTrustRegion(), SimulatedAnnealing()) | ||
resmax = maximize(x->x[1]^3+x[2]^2, [3.0, 0.0]) | ||
resmin = optimize(x->-x[1]^3-x[2]^2, [3.0, 0.0]) | ||
for meth in ( | ||
NelderMead(), | ||
BFGS(), | ||
LBFGS(), | ||
GradientDescent(), | ||
Newton(), | ||
NewtonTrustRegion(), | ||
SimulatedAnnealing(), | ||
) | ||
resmax = maximize(x -> x[1]^3 + x[2]^2, [3.0, 0.0]) | ||
resmin = optimize(x -> -x[1]^3 - x[2]^2, [3.0, 0.0]) | ||
@test Optim.maximum(resmax) == -Optim.minimum(resmin) | ||
@test resmax.res.minimum == resmin.minimum | ||
end | ||
end | ||
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prob = MVP.UnconstrainedProblems.examples["Powell"] | ||
f = objective(prob) | ||
g! = gradient(prob) | ||
h! = hessian(prob) | ||
fmax(x) = -f(x) | ||
gmax = (G, x) -> (g!(G, x); G .= -G) | ||
hmax = (H, x) -> (h!(H, x); H .= -H) | ||
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resmax_f = maximize(fmax, prob.initial_x) | ||
resmin_f = optimize(f, prob.initial_x) | ||
for prop in (:iterations, :ls_success, :minimizer, :minimum) | ||
@test getproperty(resmax_f.res, prop) == getproperty(resmin_f, prop) | ||
end | ||
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resmax_f_nm = maximize(fmax, prob.initial_x, NelderMead()) | ||
resmin_f_nm = optimize(f, prob.initial_x, NelderMead()) | ||
for prop in (:iterations, :ls_success, :minimizer, :minimum) | ||
@test getproperty(resmax_f_nm.res, prop) == getproperty(resmin_f_nm, prop) | ||
end | ||
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resmax_f_bfgs = maximize(fmax, prob.initial_x, BFGS()) | ||
resmin_f_bfgs = optimize(f, prob.initial_x, BFGS()) | ||
for prop in (:iterations, :ls_success, :minimizer, :minimum) | ||
@test getproperty(resmax_f_bfgs.res, prop) == getproperty(resmin_f_bfgs, prop) | ||
end | ||
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resmax_f_newton = maximize(fmax, prob.initial_x, Newton()) | ||
resmin_f_newton = optimize(f, prob.initial_x, Newton()) | ||
for prop in (:iterations, :ls_success, :minimizer, :minimum) | ||
@test getproperty(resmax_f_newton.res, prop) == getproperty(resmin_f_newton, prop) | ||
end | ||
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resmax_fg = maximize(fmax, gmax, prob.initial_x, BFGS()) | ||
resmin_fg = optimize(f, g!, prob.initial_x, BFGS()) | ||
for prop in (:iterations, :ls_success, :minimizer, :minimum) | ||
@test getproperty(resmax_fg.res, prop) == getproperty(resmin_fg, prop) | ||
end | ||
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resmax_fgh = maximize(fmax, gmax, hmax, prob.initial_x, Newton()) | ||
resmin_fgh = optimize(f, g!, h!, prob.initial_x, Newton()) | ||
for prop in (:iterations, :ls_success, :minimizer, :minimum) | ||
@test getproperty(resmax_fgh.res, prop) == getproperty(resmin_fgh, prop) | ||
end | ||
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end |