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Omit performance warning when creating OptimizationProblem #122

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Feb 25, 2025
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Omit performance warning when creating OptimizationProblem #122

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2 changes: 1 addition & 1 deletion src/aoe_ctc.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@

# optimization
optf = OptimizationFunction((u, p) -> f_minimize(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, fct_start, [], lb=fct_lb, ub=fct_ub)
optpro = OptimizationProblem(optf, fct_start, (), lb=fct_lb, ub=fct_ub)

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res = solve(optpro, NLopt.LN_BOBYQA(), maxiters = 3000, maxtime=optim_time_limit)
converged = (res.retcode == ReturnCode.Success)

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2 changes: 1 addition & 1 deletion src/aoefit.jl
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Expand Up @@ -179,7 +179,7 @@ function fit_single_aoe_compton(h::Histogram, ps::NamedTuple; uncertainty::Bool=

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000) #, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
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6 changes: 3 additions & 3 deletions src/aoefit_combined.jl
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Expand Up @@ -31,7 +31,7 @@ function fit_single_aoe_compton_with_fixed_μ_and_σ(h::Histogram, μ::Number,

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -128,7 +128,7 @@ function neg_log_likelihood_single_aoe_compton_with_fixed_μ_and_σ(h::Histogram
# MLE
if optimize
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -202,7 +202,7 @@ function fit_aoe_compton_combined(peakhists::Vector{<:Histogram}, peakstats::Str

# MLE
optf = OptimizationFunction((u, p) -> (f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, NLopt.LN_BOBYQA(), maxiters = 5000, maxtime=5*optim_time_limit)
converged = (res.retcode == ReturnCode.Success)
if !converged @warn "Fit did not converge" end
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2 changes: 1 addition & 1 deletion src/chi2fit.jl
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Expand Up @@ -71,7 +71,7 @@ function chi2fit(f_fit::Function, x::AbstractVector{<:Union{Real,Measurement{<:R

# minimization and error estimation
optf = OptimizationFunction((u, p) -> (f_opt ∘ inverse(f_trafo))(u), AutoForwardDiff())
optprob = OptimizationProblem(optf, f_trafo(v_init), [], lb=lower_bound, ub=upper_bound)
optprob = OptimizationProblem(optf, f_trafo(v_init), (), lb=lower_bound, ub=upper_bound)
res = solve(optprob, NLopt.LN_BOBYQA(), maxiters = 3000, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
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2 changes: 1 addition & 1 deletion src/ctc.jl
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Expand Up @@ -67,7 +67,7 @@

# optimization
optf = OptimizationFunction((u, p) -> f_minimize(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, ustrip.(e_unit, fct_start), [], lb=ustrip.(e_unit, fct_lb), ub=ustrip.(e_unit, fct_ub))
optpro = OptimizationProblem(optf, ustrip.(e_unit, fct_start), (), lb=ustrip.(e_unit, fct_lb), ub=ustrip.(e_unit, fct_ub))

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res = solve(optpro, NLopt.LN_BOBYQA(), maxiters = 3000, maxtime=optim_time_limit)
converged = (res.retcode == ReturnCode.Success)

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2 changes: 1 addition & 1 deletion src/lqfit.jl
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Expand Up @@ -84,7 +84,7 @@

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())

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res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
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10 changes: 5 additions & 5 deletions src/singlefit.jl
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Expand Up @@ -51,7 +51,7 @@

# MLE
optf = OptimizationFunction((u, p) -> (f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -170,7 +170,7 @@

# MLE
optf = OptimizationFunction((u, p) -> (f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -291,7 +291,7 @@

# MLE
optf = OptimizationFunction((u, p) -> (f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -423,7 +423,7 @@

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -529,7 +529,7 @@

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())

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res = solve(optpro, Optimization.LBFGS(), maxiters = 3000)#, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
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4 changes: 2 additions & 2 deletions src/specfit.jl
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Expand Up @@ -97,7 +97,7 @@ function fit_single_peak_th228(h::Histogram, ps::NamedTuple{(:peak_pos, :peak_fw

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000) #, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
Expand Down Expand Up @@ -301,7 +301,7 @@ function fit_subpeaks_th228(

# MLE
optf = OptimizationFunction((u, p) -> ((-) ∘ f_loglike ∘ inverse(f_trafo))(u), AutoForwardDiff())
optpro = OptimizationProblem(optf, v_init, [])
optpro = OptimizationProblem(optf, v_init, ())
res = solve(optpro, Optimization.LBFGS(), maxiters = 3000) #, maxtime=optim_time_limit)

converged = (res.retcode == ReturnCode.Success)
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